Using bi-directional communications in a market-based resource allocation system

ABSTRACT

Disclosed herein are representative embodiments of methods, apparatus, and systems for distributing a resource (such as electricity) using a resource allocation system. In one exemplary embodiment, a plurality of requests for electricity are received from a plurality of end-use consumers. The requests indicate a requested quantity of electricity and a consumer-requested index value indicative of a maximum price a respective end-use consumer will pay for the requested quantity of electricity. A plurality of offers for supplying electricity are received from a plurality of resource suppliers. The offers indicate an offered quantity of electricity and a supplier-requested index value indicative of a minimum price for which a respective supplier will produce the offered quantity of electricity. A dispatched index value is computed at which electricity is to be supplied based at least in part on the consumer-requested index values and the supplier-requested index values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/587,009, filed Sep. 29, 2009, and entitled “USING BI-DIRECTIONALCOMMUNICATIONS IN A MARKET-BASED RESOURCE ALLOCATION SYSTEM,” whichclaims the benefit of U.S. Provisional Application No. 61/194,596, filedon Sep. 29, 2008, and entitled “METHOD AND SYSTEM FOR ELECTRIC POWERGRID CONTROL,” both of which are hereby incorporated by reference intheir entirety.

ACKNOWLEDGMENT OF GOVERNMENT SUPPORT

This invention was made with Government support under ContractDE-AC05-76RLO1830 awarded by the U.S. Department of Energy. TheGovernment has certain rights in the invention.

FIELD

This application relates generally to the field of power grid managementand control.

BACKGROUND

The demand for electricity is expected to continue its historical growthtrend far into the future. To meet this growth with traditionalapproaches would require adding power generation, transmission, anddistribution that may cost in the aggregate up to $2,000/kW on theutility side of the meter. The amount of capacity in generation,transmission, and distribution generally must meet peak demand and mustprovide a reserve margin to protect against outages and othercontingencies. The nominal capacity of many power-grid assets istypically used for only a few hundred hours per year. Traditionalapproaches for maintaining the adequacy of the nation's power generationand delivery system are characterized by sizing system components tomeet peak demand, which occurs only a few hours during the year. Thus,overall asset utilization remains low, particularly for assets locatednear the end-user in the distribution portion of the system.

The increased availability of energy-information technologies can playan important role in addressing the asset utilization issuecost-effectively. It has been estimated that $57 billion savings couldbe realized by applying smart technologies throughout the nation'selectric generation, transmission, and distribution systems over thenext 20 years.

Accordingly, there is a need for improved power distribution systems andtechniques that allow larger portions of the demand-side infrastructureto function as an integrated system element. For example, there is aneed for systems and methods that enable end-use electrical devicesand/or consumers to actively participate in grid control.

SUMMARY

Disclosed below are representative embodiments of methods, apparatus,and systems for distributing a resource (such as electricity). One ofthe disclosed embodiments is a nested, hierarchical resource allocationscheme that can be applied to any system. The resource allocation schemecan, for example, utilize a match of supply and demand at multiplelevels and various locations within a transactive network, have supplyresources that can be at the highest level but may also be at lowerlevels and have demand that varies with time and location in thenetwork, and that also has a degree of response to resource allocationsignals within the network. Specific examples are disclosed herein inwhich the general scheme is applied to manage and control the centralgeneration, transmission, distribution, distributed local generation andstorage, and end-use elements of an electric power grid. Furthermore,specific techniques are described that can be used for distributedgeneration, thermostatically-controlled end-use elements with two-waycommunication capabilities and end-use elements with one-waycommunication capabilities.

One of the disclosed embodiments is a method for clearing offers andrequests as can be used in a resource allocation system. In thisembodiment, a plurality of requests for electricity are received from aplurality of end-use consumers. The requests indicate a requestedquantity of electricity and a consumer-requested index value indicativeof a maximum price a respective end-use consumer will pay for therequested quantity of electricity. A plurality of offers for supplyingelectricity are also received from a plurality of resource suppliers.Each of the offers indicates an offered quantity of electricity and asupplier-requested index value indicative of a minimum price for which arespective supplier will produce the offered quantity of electricity.Using computing hardware (e.g., a computer processor or an integratedcircuit), a dispatched index value is computed at which electricity isto be supplied based at least in part on the consumer-requested indexvalues and the supplier-requested index values. The acts of receivingthe plurality of requests for electricity, receiving the plurality ofoffers for supplying electricity, and determining can be repeated atperiodic intervals (e.g., less than 60 minutes, less than 10 minutes, orother such intervals). The act of receiving the plurality of requestsand the act of receiving the plurality of offers can be performedsubstantially simultaneously. In certain implementations, the dispatchedindex value is transmitted to at least one of the end-use consumers orresource suppliers. In some implementations, the act of determining thedispatched index value is performed using a double auction method. Forexample, the act of determining the dispatched index value can compriseseparating the requests and the offers into two groups, sorting eachitem in the two groups according to a quantity level, and determiningthe dispatched index value by determining the index value at which thesame quantity level for requests and offers occurs.

Another disclosed embodiment is a system for allocating resources. Thesystem can comprise, for example, at least two resource allocationsystems. A first of the resource allocation systems can be nested withina second of the resource allocation systems. Each of the resourceallocation systems can be configured to communicate with resourceconsumers and resource suppliers over a digital network and according toa bi-directional communication protocol. In particular implementations,the resource allocation systems dispatch resources by separatingrequests and offers for a resource into two groups, sorting each item inthe two groups according to a quantity level, and determining adispatched index value for the resource by determining the index valueat which the same quantity level for requests and offers occurs. Thesystem can additionally include a tracking system that tracks indexvalues dispatched by at least one of the resource allocation systems anddebits a consumer's index balance in response to the dispatched indexvalues. The resource allocation system can be used to distribute avariety of resources, including electricity.

Another embodiment disclosed herein is a method for computing bids usingtwo-way communication in a resource allocation system. In thisembodiment, a desired performance value indicative of a user's desiredperformance level for an electrical device is received. A user tolerancevalue indicative of the user's willingness to tolerate variations fromthe desired performance level is also received. Using computing hardware(e.g., a computer processor or an integrated circuit), a bid value forpurchasing electricity sufficient to operate the electrical device atthe desired performance level is computed. The computing can beperformed using at least the desired performance value and the usertolerance value. The electrical device can be a variety of devices, suchas an air-conditioning unit; heating unit; heating, ventilation, and airconditioning (HVAC) system; hot water heater; refrigerator; dish washer;washing machine; dryer; oven; microwave oven; pump; home lightingsystem; electrical charger, electric vehicle charger; or home electricalsystem. In certain implementations, a historical dispatch valueindicative of values at which electricity has been dispatched by themarket-based resource allocation system during a previous time period isalso computed. The computing of the bid value can be additionallyperformed using the historical dispatched value. The historical dispatchvalue can be, for example, an average of multiple dispatch values fromthe previous time period (e.g., the previous 24 hours or less). Inparticular implementations, a standard deviation of the values at whichelectricity has been dispatched by the market-based resource allocationsystem during a previous time period is computed. The computing of thebid value can be additionally performed using the standard deviation. Insome implementations, a current performance level of the electricaldevice can be received, and the computing of the bid value can beadditionally performed using the current performance level. In certainimplementations, the user tolerance value is selected from at least afirst tolerance value and a second tolerance value, the first tolerancevalue resulting in higher bid values relative to the second tolerancevalue. In some implementations, the bid value is transmitted to acentral computer in the market-based resource allocation system. Anindication of a dispatched value for a current time frame can bereceived from the central computer. The bid value can be compared to thedispatched value for the current time frame, and a signal can begenerated to activate the electrical device based on the comparison(e.g., if the bid value is equal to or exceeds the dispatched value forthe current time frame). Any of the disclosed method acts can berepeated over periods of time (e.g., time periods of 15 minutes orless).

A further embodiment disclosed herein is another method for computingbids in a resource allocation system using two-way communication. Inthis embodiment, an indication of a current status of a systemcontrolled by an electrical device is received. Using computing hardware(e.g., a computer processor or an integrated circuit), an averagedispatched value is computed using multiple dispatched values from aprevious time period, the multiple dispatched values representing valuesat which electricity was dispatched by the market-based resourceallocation system during the previous time period. Using the computinghardware, a bid value for purchasing electricity sufficient to operatethe electrical device is computed, the computing being performed usingat least the current status of the system and the average dispatchedvalue. In certain implementations, a standard deviation of the multipledispatched values from the previous time period is computed, and thecomputing of the bid value is additionally performed using the standarddeviation. In some implementations, a user comfort setting selected by auser is received. The user comfort setting can be selected from at leasta first user comfort setting and a second user comfort setting, thefirst user comfort setting indicating the user's willingness to pay moreto achieve a desired status of the system controlled by the electricaldevice relative to the second user comfort setting. In theseimplementations, the computing of the bid value can be additionallyperformed using the user comfort setting. In one particularimplementation, the electrical device is a pump and the current statusis a measurement of a water level affected by the pump. In someimplementations, the electrical device is an electric charger forcharging a battery, and the current status of the system is the state ofcharge of the battery. In one particular implementation in which theelectrical device is an electrical charger for charging a battery, thebid value is computed according to the following equation:P _(bid) =P _(avg) −kP _(std) SOC _(dev)where P_(bid) is the bid value, P_(avg) is an average daily clearingprice of energy, P_(std) is a daily standard deviation of price, andSOC_(dev) is the fractional deviation of the SOC from a desired SOC(SOC_(des)) with respect to minimum and maximum limits (SOC_(min) andSOC_(max)) set by a user. In some implementations, the bid value istransmitted to a central computer in the market-based resourceallocation system. An indication of a dispatched value for a currenttime frame can be received from the central computer. The bid value canbe compared to the dispatched value for the current time frame, and asignal can be generated to activate the electrical device based on thecomparison (e.g., if the bid value is equal to or exceeds the dispatchedvalue for the current time frame). Any of the disclosed method acts canbe repeated over fixed periods of time (e.g., time periods of 15 minutesor less).

Another embodiment disclosed herein is a method for computing a bidvalue in a resource allocation system, the bid value being related tocontrolling temperature in a temperature-controlled zone. In thisembodiment, a desired temperature value indicative of a user's desiredtemperature in a temperature-controlled zone is received. A user comfortsetting selected by the user is also received, the user comfort settingbeing selected from at least a first user comfort setting and a seconduser comfort setting, the first user comfort setting indicating theuser's willingness to pay more to achieve the desired temperaturerelative to the second user comfort setting. Using computing hardware(e.g., a computer processor or an integrated circuit, a bid value iscomputed for purchasing electricity to heat or cool thetemperature-controlled zone to the desired temperature, the bid valuebeing computed using at least the desired temperature, the user comfortsetting, and a historical value indicative of an average value at whichelectricity was dispatched by the resource allocation system over ahistorical time period. In certain implementations, the bid value can befurther computed using a standard deviation of dispatched values fromthe historical time period. In certain implementations, the historicaltime period is 24 hours. In some implementations, the first user comfortsetting is associated with a first set of elasticity factors andtemperature limits, and the second user comfort setting is associatedwith a second set of elasticity factors and temperature limits. Theelasticity factors and the temperature limits for each of the first andthe second comfort settings can include elasticity factors andtemperature limits for when the desired temperature is greater than acurrent temperature and different elasticity factors and temperaturelimits for when the desired temperature is less than the currenttemperature. In particular implementations, the selected user comfortsetting is associated with elasticity factors k_(T) _(—) _(L) and k_(T)_(—) _(H) and temperature limits T_(max) and T_(min). In suchimplementations, the bid value can be computed according to thefollowing equation,

$P_{bid} = {P_{average} + {\left( {T_{current} - T_{set}} \right)\frac{k_{T} \times \sigma}{{T_{\lim\;{it}} - T_{set}}}}}$where P_(bid) is the bid value, P_(average) is the average dispatchedvalue for the historical time period, T_(current) is a currenttemperature, T_(set) is a desired temperature set point, σ is thestandard deviation of the dispatched values from the historical timeperiod, and k_(T) and T_(limit) are either k_(T) _(—) _(L) and T_(min)if T_(set) is less than T_(current) and k_(T) _(—) _(H) and T_(max) ifT_(set) is greater than T_(current). In some implementations, the bidvalue is computed during a first time period, and the computing isrepeated during a next time period. A modified desired temperature setpoint T_(set,a) can be used for the next time period. For example, themodified desired temperature set point T_(set,a) can be computedaccording to the following equation for the next time period:

$T_{{set},a} = {T_{set} + {\left( {P_{clear} - P_{average}} \right)\;\frac{{T_{\lim\;{it}} - T_{set}}}{k_{T} \times \sigma}}}$where P_(clear) is a clearing price from the first time period. Any ofthe disclosed method acts can be repeated after a fixed time period(e.g., after a time period of 15 minutes or less).

A further embodiment disclosed herein is another method for computing abid value in a resource allocation system, the bid value being relatedto controlling temperature in a temperature-controlled zone. In thisembodiment, a current zone temperature is measured and a consequent bidprice computed. The bid price is communicated into the resourceallocation system. A resulting market clearing price is received fromthe resource allocation system. Using computing hardware, an adjustedzone set point is calculated. A thermostat's zone set point can be resetto the adjusted zone set point. In certain implementations, a customer'sbalance of available funds is debited in response to the resultingmarket clearing price.

Another embodiment disclosed herein is a method for computing bids in aresource allocation system using one-way communication. In thisembodiment, a user comfort setting selected by a user is received, theuser comfort setting being selected from at least a first user comfortsetting and a second user comfort setting, the first user comfortsetting indicating the user's willingness to pay more to achieve adesired performance level for an electrical device relative to thesecond user comfort setting. An average dispatched value is computedusing multiple dispatched values from a previous time period (e.g., atime period of 24 hours or less), the multiple dispatched valuesrepresenting values at which electricity was dispatched by themarket-based resource allocation system during the previous time period.Using computing hardware (e.g., a computer processor or an integratedcircuit), a probability value of operating the electrical device iscomputed based on at least the user comfort setting and the averagedispatched value. In certain implementations, a standard deviation ofthe multiple dispatched values from the previous time period iscomputed, and the probability value is additionally based on thestandard deviation. In certain implementations, dispatched values areperiodically received from a central computer in the resource allocationsystem. In some implementations, a random number is generated, adetermination is made as to whether to operate the electrical device(the determination being made by comparing the random number to theprobability value), and a signal for causing the electrical device tooperate is generated based on the comparison. In some implementations,the selected comfort setting is associated with a weighting factork_(W). In such implementations, the probability value can be computedaccording to the following equation:

$\begin{matrix}{r = {k_{w}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma}{\int_{- \infty}^{P_{clear}}{{\mathbb{e}}^{- \frac{{({\overset{\_}{P} - x})}^{2}}{2\sigma^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack}} \\{{= {k_{w}\left\lbrack {{N\left( {P_{clear},\overset{\_}{P},\sigma} \right)} - 0.5} \right\rbrack}};{r \geq 0}}\end{matrix}$ r = 0; otherwisewhere N is the cumulative normal distribution, P_(clear) is a currentdispatched value, P is the average dispatched value over the timeperiod, and σ is the standard deviation of the dispatched value over thetime period. The electrical device can be a wide variety of electricaldevices, including for example an air-conditioning unit; heating unit;heating, ventilation, and air conditioning (HVAC) system; hot waterheater; refrigerator; dish washer; washing machine; dryer; oven;microwave oven; pump; home lighting system; electrical charger, electricvehicle charger; or home electrical system. Any of the method acts canbe performed repeatedly after fixed time periods (e.g., time periods of15 minutes or less).

A further embodiment disclosed herein is another method for computingbids in a resource allocation system using one-way communication. Inthis embodiment, a probability that current flow to anon-thermostatically controlled device will be interrupted based uponpreselected settings and cleared market prices is determined usingcomputing hardware. In certain implementations, a customer balance isdebited in response to the cleared market prices.

Another disclosed embodiment is a method for generating offer values ina resource allocation system. In this embodiment, an offer valueindicative of a value at which electricity can be supplied by agenerator for a current time frame is computed using computing hardware,the offer value being based at least in part on dispatched values fromprevious time frames, the dispatched values representing values at whichelectricity was dispatched by the market-based resource allocationsystem during the previous time frames. The offer value is transmittedalong with a value indicative of a quantity of electricity that can besupplied by the generator during the current time frame to a centralcomputer. A message is received from the central computer indicating adispatched value for the current time frame. In certain implementations,the dispatched value is compared to the offer value, and the generatoris activated in response to the comparison. Any of these method acts canbe repeated for subsequent time frames (e.g., time frames of 15 minutesor less, 5 minutes or less, or other amount of time). In particularimplementations, the dispatched value was computed by the centralcomputer using a double auction technique. The dispatched value can becomputed by the central computer, for example, by using the offer valueand bid values transmitted from consumers to the central computer duringthe current time frame. In certain implementations, a standard deviationfor the dispatched values from the previous time frames is computed, andthe offer index value is additionally based at least in part on thestandard deviation. The offer value can also be additionally based atleast in part on a startup cost for supplying the electricity, ashutdown cost for supplying the electricity, and/or a remaining numberof time frames available in an operating license associated with theelectricity.

Another method disclosed herein is a method for generating a generatorbid value for bidding to supply electricity in a market-based resourceallocation system. In this embodiment, a generator bid value indicativeof a price at which a generator can deliver electricity into a powergrid is computed using computing hardware. The generator bid value and avalue indicative of a magnitude of electrical load the generator canremove from the power grid are transmitted to a central computer thatmanages the resource allocation system. A message is received from thecentral computer indicating whether the generator should be activated.The message from the central computer can include a current market pricevalue indicative of a current market price for electricity, and whereinthe method comprises activating the generator if the generator bid valueis less than the current market price value. In certain implementations,the generator bid value is based at least in part on a license premiumvalue that is computed using a remaining number of hours the generatorcan operate according to the generator's operating license, a fuel costvalue indicative of a cost of fuel for operating the generator for afixed period of time, a cost for operating and maintaining the generatorfor a fixed period of time, a startup cost value indicative of a costfor starting the generator, and/or a shutdown cost value indicative of acost for shutting down the generator before a minimum operating timethreshold is achieved. In particular implementations, the generator bidvalue is computed according to the following equation,bid=licensepremium·(fuelcost+O&Mcost+startupcost+shutdownpenalty),wherein bid is the generator bid, licensepremium is a value associatedwith remaining unused licensed hours for the generator, O&Mcost is avalue associated with the operating and maintenance cost, startupcost isa value associated with penalties or costs for starting up thegenerator, and shutdownpenalty is a value associated with penalties orcosts for prematurely shutting down the generator. Any of the disclosedmethod acts can be repeated over fixed periods of time (e.g., fixedperiods of 15 minutes or less).

Another embodiment disclosed herein is a method of operating anelectrical charger in a market-based resource allocation system. In thisembodiment, a user comfort setting selected by a user is received. Theuser comfort setting is selected from at least a first user comfortsetting and a second user comfort setting, the first user comfortsetting indicating the user's willingness to pay more to achieve adesired performance level for an electrical device relative to thesecond user comfort setting. An average dispatched value is computedusing multiple dispatched values from a previous time period, themultiple dispatched values representing values at which electricity wasdispatched by the market-based resource allocation system during theprevious time period. A current rate of charge at which the electricalcharger is to operate is computed (e.g., using computing hardware, suchas a computer processor or an integrated circuit) based on at least theuser comfort setting and the average dispatched value. A signal isgenerated for controlling the electrical charger such that it provides acharge at the current rate of charge. In certain implementations, astandard deviation of the multiple dispatched values is computed fromthe previous time period. In these implementations, the current rate ofcharge is additionally based on the standard deviation. In someimplementations, dispatched values are periodically received from acentral computer in the resource allocation system. In particularimplementations, the current rate of charge is computed according to thefollowing:ROC_(set)=ROC_(des)(1−kP _(dev))where ROC_(set) is the current rate of charge, ROC_(des) is a desiredrate-of-charge such that

${ROC}_{des} = \frac{\left( {{SOC}_{final} - {SOC}_{obs}} \right)}{n_{hours}}$where SOC_(final) is the final state of charge, SOC_(obs) is the currentstate of charge observed, n_(hours) is the number of hours remainingbefore the SOC_(final) is to be achieved, k is the user comfort setting,with 0<k<∞, P_(dev) is the price deviation such that

$P_{dev} = \frac{P_{now} - P_{avg}}{P_{std}}$wherein P_(now) is a current price, P_(avg) is the average dailyclearing price for energy, and P_(std) is the daily standard deviationof the price, SOC_(final) is the final desired state-of-charge of thevehicle, SOC_(obs) is the current observed state-of-charge of thevehicle, and n_(hours) is the number of hours remaining before theSOC_(final) must be achieved. Any of the actions in this embodiment canbe repeated periodically (e.g., at fixed time periods).

Embodiments of the disclosed methods can be performed using computinghardware, such as a computer processor or an integrated circuit. Forexample, embodiments of the disclosed methods can be performed bysoftware stored on one or more non-transitory computer-readable media(e.g., one or more optical media discs, volatile memory components (suchas DRAM or SRAM), or nonvolatile memory or storage components (such ashard drives)). Such software can be executed on a single computer or ona networked computer (e.g., via the Internet, a wide-area network, alocal-area network, a client-server network, or other such network).Embodiments of the disclosed methods can also be performed byspecialized computing hardware (e.g., one or more application specificintegrated circuits (ASICs) or programmable logic devices (such as fieldprogrammable gate arrays (FPGAs)) configured to perform any of thedisclosed methods). Additionally, any intermediate or final resultcreated or modified using any of the disclosed methods can be stored ona non-transitory storage medium (e.g., one or more optical media discs,volatile memory or storage components (such as DRAM or SRAM), ornonvolatile memory or storage components (such as hard drives)).Furthermore, any of the software embodiments (comprising, for example,computer-executable instructions for causing a computer to perform anyof the disclosed methods), intermediate results, or final resultscreated or modified by the disclosed methods can be transmitted,received, or accessed through a suitable communication means.

The foregoing and other objects, features, and advantages of theinvention will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary resource allocation systemthat can be nested to any arbitrary depth with consumers making demandrequests and producers making supply offers.

FIG. 2A is a block diagram showing a resource consumer who makes demandrequests to a local resource allocation system and who consumes theresource based on the dispatched allocation index. The local resourceallocation system in FIG. 2A aggregates the consumer's demand requestwith other requests.

FIG. 2B is a block diagram showing a resource producer who makes supplyoffers to a local resource allocation system and who supplies theresource based on the dispatched allocation index. The local resourceallocation system in FIG. 2B aggregates the producer's supply offer withother offers.

FIG. 3 is a graph illustrating an exemplary adaptive control strategyfor consumers using a resource allocation system as in FIG. 2A.

FIG. 4 is a graph illustrating a bid and response strategy forthermostatically controlled loads according to one exemplary embodimentof the disclosed technology.

FIG. 5 is a schematic block diagram of an exemplary building automationsystem that can be controlled according to embodiments of the disclosedtechnology

FIGS. 6A and 6B are graphs showing the effect of transactive control onzone temperatures and set points in an experiment performed using anembodiment of the disclosed technology.

FIG. 7 is graph showing the zone bid, market clearing price, and themean price of electricity for an experiment performed using anembodiment of the disclosed technology.

FIG. 8 is an image of the interior of a pump house and two 40-hp pumpsused in an experiment investigating aspects of the disclosed technology.

FIG. 9 is an image of a water reservoir associated with the pumps inFIG. 8.

FIG. 10 is an image of the exterior of the pump station housing thepumps in FIG. 8.

FIG. 11 is an image of the main control panel of the pumps in FIG. 8.

FIG. 12 is an image of the inside of one of the control panels from FIG.11.

FIG. 13 is a schematic block diagram of the control system used tocontrol water pumps in an experiment investigating aspects of thedisclosed technology.

FIG. 14 is a graph showing the bid strategy implemented for one of thepumps in the control system of FIG. 13 relative to the height of itscorresponding reservoir.

FIG. 15 is a graph showing the reservoir level during a sample test dayfor a reservoir in the system of FIG. 13.

FIG. 16 is a graph showing the bid and price for one of the pumps in thesystem of FIG. 13 during a sample test day.

FIG. 17 is a graph showing the number of pumps operating in the systemof FIG. 13 during a sample test day.

FIG. 18 is an image of a 600-kW Caterpillar diesel generator used in anexperiment investigating aspects of the disclosed technology.

FIG. 19 is an image of a 175-kW Kohler generator used in the experimentinvestigating aspects of the disclosed technology.

FIG. 20 is an image of the automatic transfer switch coupled to thegenerator shown in FIG. 19.

FIG. 21 is a schematic block diagram of the control system used tocontrol generator activation in an experiment investigating aspects ofthe disclosed technology.

FIG. 22 is a graph showing the distribution of accepted generator bidprices for the two diesel generator used in the experiment shown in FIG.21.

FIG. 23 is a graph showing the distribution of capacity bids for the twodiesel generators used in the experiment shown in FIG. 21.

FIG. 24 is a graph showing the average site loads by hour of day for theexperiment shown in FIG. 21.

FIG. 25 is an image of a distributed energy resource dashboard that canbe used to monitor aspects of a resource allocation system implementedaccording to an embodiment of the disclosed technology.

FIG. 26 is a graph of a curve showing the duration of feeder capacityduring a 750-kW feeder constraint when power was managed using anembodiment of the disclosed technology.

FIG. 27 is a graph illustrating the shifting of thermostaticallycontrolled load by price as a result of performing resource allocationaccording to an embodiment of the disclosed technology.

FIG. 28 is a graph showing the total distributed power generationthrough a day when resource allocation was performed according to anembodiment of the disclosed technology.

FIG. 29 is a schematic block diagram showing a project communicationschematic as was used during an experimental use of an embodiment of thedisclosed technology.

FIG. 30 is a graph showing an example 3-day history for a 5-minutetwo-sided clearing market according to an embodiment of the disclosedtechnology.

FIGS. 31A and 31B are graphs showing the control of an imposeddistribution constraint using transactive control according to anembodiment of the disclosed technology.

FIG. 32 is a graph showing consumer preferences for contract types aspart of an experimental use of embodiments of the disclosed technology.

FIG. 33 is an image showing components of an energy management systemthat was used during an experimental use of embodiments of the disclosedtechnology.

FIG. 34 is an image of a load control module and an experimentparticipant's water heater.

FIG. 35 is an image of an energy meter as used by an experimentparticipant.

FIG. 36 is an image of a dryer configured to display energy alertsignals according to embodiments of the disclosed technology.

FIG. 37 is an image of an experiment participant accessing a web sitefor configuring his power usage preferences according to an embodimentof the disclosed technology.

FIG. 38 is an image of a screen shot of a web site configured to allow auser to input power usage preferences according to an embodiment of thedisclosed technology.

FIG. 39 is a graph showing a distribution of selected residentialthermostat limit setting from participants in an experimental use of anembodiment of the disclosed technology.

FIG. 40 is a graph showing network telemetry performance statisticsduring an experimental use of an embodiment of the disclosed technology.

FIG. 41 is a graph showing monthly incentive payment distributions madeto experiment participants.

FIG. 42 is a graph showing monthly savings estimates by contract typefor experiment participants.

FIG. 43 is a graph showing average monthly energy use for experimentparticipants.

FIG. 44 is a graph showing monthly utility revenue by contract type forexperiment participants.

FIG. 45 is a graph showing average monthly energy prices by contracttype for experiment participants.

FIGS. 46A through 46D are graphs showing MIDC wholesale electricityprice behavior during an experimental use of an embodiment of thedisclosed technology. FIG. 46A shows hourly prices. FIG. 47A shows dailyfirm wholesale power prices. FIG. 47C shows a wholesale priceprobability distribution. FIG. 47D shows a wholesale price durationdistribution.

FIGS. 47A through 47D are graphs showing diurnal residential load shapesby contract type for experiment participants. FIG. 47A is for a winterweekday. FIG. 47B is for a winter weekend. FIG. 47C is for a springweekday. FIG. 47D is for a spring weekend. FIG. 47E is for a summerweekend. FIG. 47F is for a summer weekend.

FIGS. 48A through 48B are graphs showing real-time market shifting of athermostatically controlled residential load as was experienced duringan experimental use of an embodiment of the disclosed technology. FIG.48A shows demand during unconstrained supply conditions, and FIG. 48Bshows demand during constrained supply conditions.

FIGS. 49A and 49B are graphs showing served and managed distributionload during an experimental use of an embodiment of the disclosedtechnology. FIG. 49A shows demand during moderately constrained supplyconditions, and FIG. 49B shows demand during heavily constrained supplyconditions.

FIGS. 50A through 50C are graphs showing how real-time price can flattenload.

FIG. 51 is a graph showing distribution operations during criticallyconstrained feeder conditions during an experiment use of an embodimentof the disclosed technology.

FIGS. 52A through 52C show feeder load duration curves as wereexperienced during an experimental use of an embodiment of the disclosedtechnology. FIG. 52A shows the 1500-kW feeder capacity during thesummer. FIG. 52 B shows the 750-kW feeder capacity during the winter.FIG. 52C shows the 500-kW feeder capacity during the fall.

FIG. 53 is a graph showing peak reduction and imposed feeder capacitiesduring an experimental use of an embodiment of the disclosed technology.

FIGS. 54A through 54C are graphs showing supply duration curves as wereexperienced during an experimental use of embodiments of the disclosedtechnology.

FIG. 54A shows the 500-kW feeder capacity during the fall. FIG. 54Bshows the 650-kW feeder capacity during the winter. FIG. 54C shows the1500-kW feeder capacity during the summer.

FIG. 55 is a graph illustrating the definition of consumer surplus usinga market closing diagram.

FIG. 56 is a graph showing consumer surplus by month during anexperimental use of an embodiment of the disclosed technology.

FIG. 57 is a graph showing seasonal consumer surplus by hour of dayduring an experimental use of an embodiment of the disclosed technology.

FIGS. 58A and 58B are graphs showing dispatched distributed generationduring an experimental use of an embodiment of the disclosed technology.FIG. 58A shows dispatched distributed generation by month. FIG. 58Bshows dispatched distributed generation by hour of day.

FIG. 59 is a graph illustrating the concept of an efficient frontier andother portfolio weightings.

FIG. 60 is a graph illustrating two pure distribution curves anddistributions for mixes of the two.

FIG. 61 is a graph showing efficient frontier mixtures of two puredistributions.

FIG. 62 is a graph showing peak energy use for season and time of dayfor the duration of an experimental use of an embodiment of thedisclosed technology.

FIG. 63 is a graph showing a gross margin of utility per hour perexperiment participant.

FIG. 64 is a schematic block diagram of a computing environment that canbe used to implement embodiments of the disclosed technology.

FIG. 65 is a schematic block diagram of a network topology that can beused to implement embodiments of the disclosed technology.

FIG. 66 is a flowchart showing a generalized method for clearing offersand requests as can be used in any of the disclosed resource allocationsystems.

FIG. 67 is a flowchart showing a general embodiment for computing bidsin any of the disclosed recourse allocation system using two-waycommunications.

FIG. 68 is a flowchart showing another general embodiment for computingbids in any of the disclosed recourse allocation system using two-waycommunications.

FIG. 69 is a flowchart showing a general embodiment for computing bidsin any of the disclosed recourse allocation system using one-waycommunications.

FIG. 70 is a flowchart showing a general embodiment for generating offervalues for use in any of the disclosed recourse allocation systems.

DETAILED DESCRIPTION I. General Considerations

Disclosed below are representative embodiments of methods, apparatus,and systems for distributing a resource (such as electricity) using aresource allocation system. The disclosed methods, apparatus, andsystems should not be construed as limiting in any way. Instead, thepresent disclosure is directed toward all novel and nonobvious featuresand aspects of the various disclosed embodiments, alone and in variouscombinations and subcombinations with one another. The disclosedmethods, apparatus, and systems are not limited to any specific aspector feature or combination thereof, nor do the disclosed embodimentsrequire that any one or more specific advantages be present or problemsbe solved.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed methods can be used in conjunction with other methods.Additionally, the description sometimes uses terms like “determine” and“generate” to describe the disclosed methods. These terms are high-levelabstractions of the actual operations that are performed. The actualoperations that correspond to these terms may vary depending on theparticular implementation and are readily discernible by one of ordinaryskill in the art.

Any of the disclosed methods can be implemented as computer-executableinstructions stored on one or more computer-readable media (e.g.,non-transitory computer-readable media, such as one or more opticalmedia discs, volatile memory components (such as DRAM or SRAM), ornonvolatile memory components (such as hard drives)) and executed on acomputer (e.g., any commercially available computer). Any of thecomputer-executable instructions for implementing the disclosedtechniques (e.g., the disclosed bid generation, offer generation, ordispatch index generation techniques) as well as any data created andused during implementation of the disclosed resource allocation systemscan be stored on one or more computer-readable media (e.g.,non-transitory computer-readable media). The computer-executableinstructions can be part of, for example, a dedicated softwareapplication or a software application that is accessed or downloaded viaa web browser. More specifically, such software can be executed on asingle computer (e.g., any suitable commercially available computer) orin a network environment (e.g., via the Internet, a wide-area network, alocal-area network, a client-server network, or other such network).

For clarity, only certain selected aspects of the software-basedimplementations are described. Other details that are well known in theart are omitted. For example, it should be understood that the disclosedtechnology is not limited to any specific computer language or program.For instance, the disclosed technology can be implemented by softwarewritten in C++, Java, Perl, JavaScript, Adobe Flash, or any othersuitable programming language. Likewise, the disclosed technology is notlimited to any particular computer or type of hardware. Details ofsuitable computers and hardware are well known and need not be set forthin detail in this disclosure.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions for causing a computer toperform any of the disclosed methods) can be uploaded, downloaded, orremotely accessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

The disclosed methods can also be implemented by specialized computinghardware that is configured to perform any of the disclosed methods. Forexample, the disclosed methods can be implemented by an integratedcircuit (e.g., an application specific integrated circuit (ASIC) orprogrammable logic device (PLD), such as a field programmable gate array(FPGA)). The integrated circuit can be embedded in or directly coupledto an electrical device (or element) that is configured to interact withthe resource allocation system. For example, the integrated circuit canbe embedded in or otherwise coupled to a generator, an air-conditioningunit; heating unit; heating, ventilation, and air conditioning (HVAC)system; hot water heater; refrigerator; dish washer; washing machine;dryer; oven; microwave oven; pump; home lighting system; electricalcharger, electric vehicle charger; or home electrical system.

FIG. 64 illustrates a generalized example of a suitable computingenvironment 6400 in which several of the described embodiments can beimplemented. The computing environment 6400 is not intended to suggestany limitation as to the scope of use or functionality of the disclosedtechnology, as the techniques and tools described herein can beimplemented in diverse general-purpose or special-purpose environmentsthat have computing hardware.

With reference to FIG. 64, the computing environment 6400 includes atleast one processing unit 6410 and memory 6420. In FIG. 64, this mostbasic configuration 6430 is included within a dashed line. Theprocessing unit 6410 executes computer-executable instructions and maybe a real or a virtual processor. In a multi-processing system, multipleprocessing units execute computer-executable instructions to increaseprocessing power. The memory 6420 may be volatile memory (e.g.,registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flashmemory, etc.), or some combination of the two. The memory 6420 storessoftware 6480 implementing one or more of the described techniques foroperating or using the disclosed resource allocation systems. Forexample, the memory 6420 can store software 6480 for implementing any ofthe disclosed bidding or offer strategies described herein and theiraccompanying user interfaces.

The computing environment can have additional features. For example, thecomputing environment 6400 includes storage 6440, one or more inputdevices 6450, one or more output devices 6460, and one or morecommunication connections 6470. An interconnection mechanism (not shown)such as a bus, controller, or network interconnects the components ofthe computing environment 6400. Typically, operating system software(not shown) provides an operating environment for other softwareexecuting in the computing environment 6400, and coordinates activitiesof the components of the computing environment 6400.

The storage 6440 can be removable or non-removable, and includesmagnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any othertangible non-transitory storage medium which can be used to storeinformation and which can be accessed within the computing environment6400. The storage 6440 can also store instructions for the software 6480implementing any of the described techniques, systems, or environments.

The input device(s) 6450 can be a touch input device such as a keyboard,mouse, pen, or trackball, a voice input device, a scanning device, oranother device that provides input to the computing environment 6400.The output device(s) 6460 can be a display, printer, speaker, CD-writer,or another device that provides output from the computing environment6400.

The communication connection(s) 6470 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,resource allocation messages or data, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia include wired or wireless techniques implemented with anelectrical, optical, RF, infrared, acoustic, or other carrier.

The various methods, systems, and interfaces disclosed herein can bedescribed in the general context of computer-readable instructionsstored on one or more computer-readable media. Computer-readable mediaare any available media that can be accessed within or by a computingenvironment. By way of example, and not limitation, with the computingenvironment 6400, computer-readable media include tangiblenon-transitory computer-readable media such as memory 6420 and storage6440.

The various methods, systems, and interfaces disclosed herein can alsobe described in the general context of computer-executable instructions,such as those included in program modules, being executed in a computingenvironment on a target real or virtual processor. Generally, programmodules include routines, programs, libraries, objects, classes,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. The functionality of theprogram modules may be combined or split between program modules asdesired in various embodiments. Computer-executable instructions forprogram modules may be executed within a local or distributed computingenvironment.

An example of a possible network topology for implementing a resourceallocation system according to the disclosed technology is depicted inFIG. 65. Networked computing devices 6520, 6522, 6530, 6532 can be, forexample, computers running browser or other software for accessing oneor more central computers 6510 that manage and operate the resourceallocation system. The computing devices 6520, 6522, 6530, 6532 and thecentral computer 6510 can have computer architectures as shown in FIG.64 and discussed above. The computing devices 6520, 6522, 6530, 6532 arenot limited to traditional personal computers but can comprise othercomputing hardware configured to connect to and communicate with anetwork 6512 (e.g., specialized computing hardware associated with anelectrical device or a power generator (e.g., hardware comprising anintegrated circuit (such as an ASIC or programmable logic device)configured to perform any of the disclosed methods)).

In the illustrated embodiment, the computing devices 6520, 6522, 6530,6532 are configured to connect to one or more central computers 6510. Incertain implementations, the central computer receives resource bids orrequests from those computing devices associated with resource consumers(e.g., devices 6520, 6522) and receives resource offers from thosecomputing devices associated with resource suppliers (e.g., devices6530, 6532). The one or more central computers 6510 then compute a valueat which the resource is to be dispatched (e.g., using a double auctiontechnique) and transmit this dispatched value to the computing devices6520, 6522, 6530, 6532. As more fully explained below, this process canbe repeated at fixed intervals (e.g., intervals of 10 minutes or less).In the illustrated embodiment, the central computers 6510 are accessedover a network 6512, which can be implemented as a Local Area Network(LAN) using wired networking (e.g., the Ethernet IEEE standard 802.3 orother appropriate standard) or wireless networking (e.g. one of the IEEEstandards 802.11a, 802.11b, 802.11g, or 802.11n or other appropriatestandard). Alternatively, and most likely, at least part of the network6512 can be the Internet or a similar public network.

The various possible roles and functionalities of the computing devices6520, 6522, 6530, 6532 and the one or more central computers 6510 willbe described in more detail in the following sections.

II. Introduction to Embodiments of the Disclosed Resource AllocationScheme

A. General Case of a Nested, Hierarchical Resource Allocation Schema

FIG. 1 is a schematic block diagram illustrating an embodiment of aresource allocation system 100 according to the disclosed technology. Inthe embodiment shown in FIG. 1, system 100 comprises multiple nestedresource allocation systems (two of which are shown as subsystems 110and 112), which themselves comprise self-similar resource allocationsubsystems. The resource allocation system 100 can be nested to anyarbitrary depth, with net producers (such as net producer 120) makingsupply offers and net consumers (such as net consumer 122) making demandrequests to a larger bulk system 140. All resources that are limited insome manner and can be measured can be allocated independently in such asystem. The embodiments disclosed herein generally concern applying theresource allocation system 100 to an electrical power grid in whichelectrical power is limited, but it is to be understood that thisapplication is not limiting. The resource allocation system can be usedin other contexts as well, including water supply, Internet bandwidthdistribution, or other such markets having limited resources.

In the illustrated embodiment, each of the resource allocation systemsoperates by periodically collecting demand requests from consumers andsupply offers from resource suppliers and determining an index value atwhich the resource allocation is to be dispatched. As more fullyexplained below, the dispatched index value (or allocation index value)is determined from index values associated with the demand requests andsupply offers. In one particular embodiment, the process is differentthan traditional markets in that an index that is capable of beingmonetized (rather than just a currency value itself) is used. The indexprovides a common valuation method for participants in the system. Theindex may itself be a currency, but in the absence of a single currency,a separate market can be operated to trade units of the index. Forinstance, one such index unit might be units of CO₂. Thus, in thisexample, instead of trading resources using money, participants can haveresources allocated using indexes having units of CO₂. Participants canthen use a separate traditional market to monetize the units of CO₂.Other index units are also possible, including index units that areunique to the resource allocation system but that are capable of beingmonetized. For ease of presentation, reference will sometimes be made inthis disclosure to the index for a resource as though it were the actualprice of the resource. It is to be understood that such referenceincludes not only the situation where the index is the currency, butalso the situation where the index is another index unit that is capableof being monetized or traded.

B. Participants and Accounts

In one embodiment of the disclosed technology, participants in thesystem have accounts in which the fund of index units at their disposalis kept. As consumers use resources, their index fund balances aredebited, and as producers deliver resources, their index fund balancesare credited. Index funds can be credited using a variety of mechanisms,including up-front deposits (e.g., through incentives), periodicdeposits (e.g., with income), or purchased funds from a separate indexfund market when producers sell funds.

C. Supply Offers and Demand Requests

In one exemplary embodiment of the disclosed technology, end-useconsumers use computer agents to request resources from their localdistribution service provider based on the current needs of theirend-use appliance or electrical device. For example, end-use consumerscan input their resource requests through a web site that transmits theuser's requests over the Internet to one or more central computers thatare used by the distribution service provider to allocate the resource.In such instances, the requests can be computed and transmitted byexecuting computer-executable instructions stored in non-transientcomputer-readable media (e.g., memory or storage). Alternatively, aconsumer's end-use appliances or electrical devices can be configured tothemselves compute the resource requests (in which case the appliance ordevice can be considered as the end-use consumer). In such instances,the requests can be computed using computing equipment embedded in theappliances or electrical devices themselves. The computing equipment cancomprise a computer system (e.g., a processor and non-transientcomputer-readable media storing computer-executable instructions) or cancomprise a specialized integrated circuit configured to compute theresource request (e.g., an ASIC or programmable logic device). If therequests are computed by the appliances or electrical devicesthemselves, the requests can be directly sent to the central computersof the distribution service provider (e.g., via the Internet) or can beaggregated with other requests (e.g., using a computer at the consumer'shome). For instance, the appliances and electrical devices at theconsumer's home can transmit their requests (e.g., wirelessly usingWi-Fi or the like) to a local computer, which aggregates the requests.The aggregated requests can then be sent together to the distributionservice provider (e.g., as a single request to the central computer oras a single message comprises a string of requests).

In one exemplary embodiment, resource requests comprise two pieces ofinformation: the quantity of any number of resources desired (describedas a rate of consumption for the time frame over which the resource willbe allocated) and the maximum index value at which it will be consumed.Desirably, consumers submit at least one such request for each timeframe in which they wish to consume, and the time frame is determined bythe distribution service provider. The time frame can vary fromembodiment to embodiment, but in some embodiments is 60 minutes or less,15 minutes or less, or 5 minutes or less, and some embodiments can usemixed time and/or overlapping frames. As more fully explained below, thetime frame can depend on the size of the resource allocation system andthe number of nested resource allocation systems within the overallsystem. In general, the time frame used in a lower-level system in anested framework will typically be less than the time frame for ahigher-level system in the nested framework. After receiving suchrequests within the time frame, the distribution service provider cancompute and dispatch the index value at which each resource isallocated. This value is sometimes referred to herein as the “dispatchedindex value” or “dispatched value.”

In one exemplary embodiment, resource suppliers use computer agents tosubmit offers for resources to the local distribution service providerbased on the current cost of provide the resources. For example, thesupply offers can be computed and submitted over the Internet using acomputer system (e.g., using a dedicated web site). Alternatively, thesupply offer can be computed and transmitted using a specializedintegrated circuit configured to compute the resource offer (e.g., anASIC or programmable logic device). Any such computing hardware can becoupled directly to and provide control over the relevant equipment forsupplying the resource. For instance, the computing hardware can beintegrated with the control equipment for an electrical power generator,thereby allowing the computing hardware to directly activate anddeactivate the generator as needed.

In one exemplary embodiment, offers comprise at least two pieces ofinformation: the quantity of any number of resources available(described as a rate of production for the time frame over which theresource will allocated) and the minimum index value at which it will beproduced. Producers desirably submit at least one such offer for eachtime frame in which they wish to produce resources, and the time frameis determined by the service provider.

In one exemplary embodiment for operating the resource allocationsystem, consumers are required to consume the resources which theyrequested only if they requested the resource at an index value greaterthan or equal to the dispatched index value. Conversely, consumers areprohibited from consuming the resources if they requested the resourceat an index value less than the dispatched index value for that timeframe. These rules can be enforced, for example, at the appliance orelectrical device level (e.g., using appropriate shut-off hardware) orenforced by control signals sent from a computer at the consumer's homeor locale to the relevant appliance or equipment. Violation of theserules can be subject to a penalty (e.g., a penalty levied against theoffender's index fund account). Furthermore, in some embodiments of thedisclosed technology, consumers can submit unconditional requests thatrequire the distribution service provider to deliver the resource at anyprice, and require the consumer to accept it at any price.

Similarly, in one exemplary embodiment for operating the resourceallocation system, producers are required to produce the resources whichthey offered only if they offered the resource at an index value lessthan or equal to the dispatched index value. Conversely, producers areprohibited from producing resource if they offered the resource at anindex value greater than the dispatched index value for that time frame.Violation of the rules can be subject to a penalty levied against theirindex fund accounts. Furthermore, in some embodiments of the disclosedtechnology, producers can submit unconditional offers that require thedistribution service provider to accept the resource at any price, andrequire the producer to supply it at any price.

D. Aggregation Services

In certain embodiments of the disclosed technology, and as noted above,a service provider may in turn be a consumer or producer with respect toanother service provider, depending on whether they are a net importeror exporter of resources. Examples of such arrangements are shown inblock diagrams 200 and 202 of FIG. 2A of 2B. In particular, FIG. 2Ashows a local resource consumer 210 that makes demands on a localdistribution service provider 212, who in turn aggregates local requeststo make an aggregated bulk request on a bulk distribution serviceprovider 214. FIG. 2B shows a local producer 220 that makes offers to alocal distribution provider 222, who in turn aggregates local offers tomake an aggregated offer on a bulk distribution provider 224. Any numberof service providers can be combined to construct a system of arbitrarysize and complexity.

In certain embodiments of the disclosed technology, producers andconsumers can make non-firm offers and requests as well, but suchrequests can have an index premium with respect to the firm offers andrequests presented during a given time frame. The premium can be based,for example, on the difference between the aggregate cost of loadfollowing in the service providers system and the cost the same in thebulk system (load following service cost arbitrage).

E. Multiple Time Frames

As resources are aggregated to larger and larger system, the time frameover which allocation is performed can be lengthened. For example,building resources might be dispatched on a 1 minute time frame,distribution resources on a 5 minute time frame, transmission resourceson a 15 minute time frame, and generation on a 1 hour time frame. Thispermits aggregators to also aggregate over time by exchanging or movingblocks of resources against across time frames using storage capacitiesand ramp rates.

Both consumers and producers can break their total demand and supplyinto multiple requests and offers spanning multiple time frames. Forexample, in the face of 10% uncertainty (or other percentage ofuncertainty) in the quantity needed a consumer can request the meanquantity of the needed resources in a longer time frame at any price andexchange (buy or sell) the remaining 10% fluctuation (or otherpercentage of fluctuation) in a shorter time frame at any price.

F. An Exemplary Resource Allocation Strategy

In each time frame, the index value and quantity allocated is determinedby the resource allocation service. A wide variety of methods can beused to determine the dispatched index value. In certain embodiments,however, the dispatched index is determined using a double auctiontechnique. For instance, in one particular embodiment, the followingtechnique is used. The requests and offers are separated into twogroups. Each is sorted by the index value provided, requests beingsorted by descending value, and offers by ascending value (or viceversa). Next, each item in the sorted lists is given a quantity levelcomputed by adding its quantity to the previous item's quantity level,with the first items quantity level being its quantity alone. Finally,the dispatched index value is found by determining the index value atwhich the same quantity level for requests and offers occurs. In oneembodiment, this can occur in one of two ways. Either two requests bounda single offer, in which case the supplier is required to supply lessthan the offered quantity and the offer index is the dispatched index;or two offers bound a single request, in which case the consumer isrequired to consume less than the request quantity with only partialresources and the request index value is the dispatched index.Additionally, there are some special cases that although rare must behandled explicitly. Whenever both consumers and suppliers mutually boundeach other at a given quantity level, the dispatched index can be themean of the offer and request indexes, the request index, or the offerindex. In certain embodiments, the method that maximizes the totalbenefit (e.g., profit) to both consumers and producers is chosen and incases where more than one index level maximizes the total benefit, theindex level which most equitably divides the total benefit betweenconsumers and producers is chosen.

FIG. 66 is a flowchart 6600 showing a generalized method for clearingoffers and requests as can be used in any of the disclosed resourceallocation systems. The particular method shown in FIG. 66 is for asystem for allocating electricity resources, but this usage should notbe construed as limiting. The method can be performed using computinghardware (e.g., a computer processor or an integrated circuit). Forinstance, the method can be performed by a central computer that managesthe resource allocation system.

At 6610, a plurality of requests for electricity are received from aplurality of end-use consumers (e.g., electrical devices or homeconsumers). The requests can comprise data messages indicating arequested quantity of electricity and a consumer-requested index valueindicative of a maximum price a respective end-use consumer will pay forthe requested quantity of electricity.

At 6612, a plurality of offers for supplying electricity are receivedfrom a plurality of resource suppliers. The offers can comprise datamessages indicating an offered quantity of electricity and asupplier-requested index value indicative of a minimum price for which arespective supplier will produce the offered quantity of electricity.

At 6614, a dispatched index value is computed at which electricity is tobe supplied based at least in part on the consumer-requested indexvalues and the supplier-requested index values. In some implementations,the act of determining the dispatched index value is performed using adouble auction method. For example, the act of determining thedispatched index value can comprise separating the requests and theoffers into two groups, sorting each item in the two groups according toa quantity level, and determining the dispatched index value bydetermining the index value at which the same quantity level forrequests and offers occurs.

At 6616, the dispatched index value is transmitted to at least one ofthe end-use consumers or resource suppliers (e.g., using suitablecommunication means, such as the Internet or other network).

Methods acts 6610, 6612, 6614, and 6616 can be repeated at periodicintervals (e.g., intervals of less than 60 minutes, less than 10minutes, or other such intervals). Furthermore, it should be understoodthat the method acts 6610 and 6612 do not necessarily occur in theillustrated sequence. Instead, the orders and requests can be receivedsubstantially simultaneously. For instance, the orders and requests canbe received at various times and/or orders within a given time periodand before the dispatched index is determined.

G. Demand Strategies

In some cases, suppliers or consumers desirably place offers or bidsthat nearly guarantee that they obtain consumers and suppliers,respectively. To help generate an offer or request that has a highlikelihood of being accepted by the local resource allocation system, asupplier or consumer can use recent history of dispatched indexes toforecast the most likely dispatched index for a particular offer orrequest time frame. This ability to adjust a request or offer allows aconsumer or supplier to utilize an adaptive bidding or offer strategy.As more fully illustrated below, such adaptive strategies are useful ina variety of settings, including in the heating or cooling of buildingsusing thermostatically-controlled equipment.

One possible adaptive request strategy is to compute the average and thestandard deviation of the dispatched index over the last N time frames,where N is a relatively large number compared to the time frame (e.g.,20, 50, 100 or more). When consumers cycle their demand for resourcesperiodically, then they can adjust the consumption time to exploit timeswhen the index is low. In one particular embodiment, the controldecision for consumption can be offset by the index average and scaledby the index standard deviation before being submitted to the resourceallocation system. This exemplary request strategy is illustrated ingraph 300 of FIG. 3, which is explained in greater detail through theexample shown in FIG. 4 and discussed below.

In some embodiments, the last N time frames that are used areconsecutive time frames. For instance, if N is selected to account forthe previous 24 hours, if the duration of a time frame is 5 minutes, andif the current time frame is 3:00 p.m., then the dispatched index fromthe 3:00 p.m. time frame the previous day, the index from the 3:05 p.m.time frame the previous day, the index from the 3:10 p.m. time frame theprevious day, and so on, can be used. In other embodiments, the last Ntime frames that are used are from the same time frame (or similar timeframe) as the current time frame but are from different days (e.g.,consecutive days). For instance, if N is selected to account for theprevious 7 days, if the duration of a time frame is 5 minutes, and ifthe current time frame is 3:00 p.m., then the dispatched index from the3:00 p.m. time frame from the previous 7 days can be used. Variouscombinations of these time frames can also be used (e.g., the indexvalues for multiple time frames around the current time frame frommultiple previous days). This flexibility can help further account forvariations in demand that arise throughout a day.

Many consumers that employ adaptive control also use a similar strategyto determine the operating point from the dispatched index. This is doneby simply reversing the process shown in FIG. 3, and adjusting thecontrol set-point based on the dispatched index.

The following paragraphs introduce general embodiments for generatingbid values in a resource allocation system, such as any of the resourceallocation systems disclosed herein. Specific implementations of thesegeneralized embodiments are introduced in Section III below.

FIG. 67 is a flowchart 6700 showing a general embodiment for computingbids in any of the disclosed recourse allocation system using two-waycommunications. The particular method shown in FIG. 67 is for anelectrical device in a system for allocating electrical resources, butthis usage should not be construed as limiting. The electrical devicecan be a variety of devices, such as an air-conditioning unit; heatingunit; heating, ventilation, and air conditioning (HVAC) system; hotwater heater; refrigerator; dish washer; washing machine; dryer; oven;microwave oven; pump; home lighting system; electrical charger, electricvehicle charger; or home electrical system. The method of FIG. 67 can beperformed using computing hardware (e.g., a computer processor or anintegrated circuit). For instance, the method can be performed by acomputer at an end-user's locale or home, a computer coupled to anelectrical device, or by specialized hardware (e.g., an ASIC orprogrammable logic device) coupled to the electrical device.Furthermore, it should be understood that the method acts in FIG. 67 donot necessarily occur in the illustrated sequence.

At 6710, a desired performance value indicative of a user's desiredperformance level for an electrical device is received. For example, adesired temperature for a temperature-controlled environment can bereceived.

At 6712, a user tolerance value indicative of the user's willingness totolerate variations from the desired performance level is also received.For example, a comfort setting reflective of comfort versus economy(such as any of the comfort settings shown in Table 7 or similar comfortsetting) can be received. In certain embodiments, the user tolerancevalue is selected from at least a first tolerance value and a secondtolerance value, the first tolerance value resulting in higher bidvalues relative to the second tolerance value. The performance value anduser tolerance value can be input, for example, through an appropriategraphical user interface displayed on a computer or through a keypad,touch screen, dial, or other control mechanism associated with theelectrical device.

At 6714, a bid value for purchasing electricity sufficient to operatethe electrical device at the desired performance level is computed. Inthe illustrated embodiment, the bid value is based at least in part onthe desired performance value and the user tolerance value. In certainembodiments, a historical dispatch value indicative of values at whichelectricity has been dispatched by the market-based resource allocationsystem during a previous time period is also computed. In suchembodiments, the bid value can be additionally based at least in part onthe historical dispatched value. The historical dispatched value can be,for example, an average of multiple dispatch values from the previoustime period (e.g., the previous 24 hours or less). In some embodiments,a standard deviation of the values at which electricity has beendispatched by the market-based resource allocation system during aprevious time period is also computed. In such embodiments, the bidvalue can also be based at least in part on the standard deviation. Infurther embodiments, a current performance level of the electricaldevice can also be received. In such embodiments, the bid value can alsobe based at least in part on the current performance level.

At 6716, the bid value is transmitted to a central computer in themarket-based resource allocation system (e.g., using suitablecommunication means, such as the Internet or other network).

At 6718, an indication of a dispatched value for a current time frame isreceived from the central computer.

At 6720, the bid value is compared to the dispatched value for thecurrent time frame, and a signal is generated to activate the electricaldevice based on this comparison (e.g., if the bid value is equal to orexceeds the dispatched value for the current time frame).

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, or other such time period). In certain embodiments, some of thereceived values are reused for subsequent time frames. For example, theuser-selected performance value and user tolerance value can be storedand reused for subsequent time frames. In such instances, method acts6710 and 6712 need not be repeated.

FIG. 68 is a flowchart 6800 showing another general embodiment forcomputing bids in any of the disclosed recourse allocation system usingtwo-way communications. The method in FIG. 68 can be performed bycomputing devices like those mentioned above with respect to FIG. 67.Likewise, the bids computed by the method in FIG. 68 can be associatedwith electrical devices like those mentioned above. In one particularembodiment, the electrical device is a pump.

At 6810, an indication of a current status of a system controlled by anelectrical device is received. For example, if the electrical device isa pump, the current status of the system can be a measurement of a waterlevel affected by the pump.

At 6812, an average dispatched value is computed using multipledispatched values from a previous time period. The multiple dispatchedvalues can be values at which electricity was dispatched by themarket-based resource allocation system during the previous time period.

At 6814, a bid value for purchasing electricity sufficient to operatethe electrical device is computed. In the illustrated embodiment, thebid value is based at least in part on the current status of the systemand the average dispatched value. In certain embodiments, a standarddeviation of the multiple dispatched values from the previous timeperiod is also computed. In such embodiments, the bid value isadditionally based at least in part on the standard deviation. In someembodiments, a user comfort setting selected by a user is also received.The user comfort setting can be selected from at least a first usercomfort setting and a second user comfort setting, the first usercomfort setting indicating the user's willingness to pay more to achievea desired status of the system controlled by the electrical devicerelative to the second user comfort setting. In these embodiments, thebid value is additionally based at least in part on the user comfortsetting. The desired status and user tolerance value can be input, forexample, through an appropriate graphical user interface displayed on acomputer or through a keypad, touch screen, dial, or other controlmechanism associated with the electrical device.

At 6816, the bid value is transmitted to a central computer in themarket-based resource allocation system (e.g., using suitablecommunication means, such as the Internet or other network).

At 6818, an indication of a dispatched value for a current time framecan be received from the central computer.

At 6820, the bid value is compared to the dispatched value for thecurrent time frame, and a signal is generated to activate the electricaldevice based on this comparison (e.g., if the bid value is equal to orexceeds the dispatched value for the current time frame).

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, or other such time period). In certain embodiments, some of thereceived values are reused for subsequent time frames. For example, theuser comfort setting can be stored and reused for subsequent timeframes.

FIG. 69 is a flowchart 6900 showing a general embodiment for computingbids in any of the disclosed recourse allocation system using one-waycommunications. The particular method shown in FIG. 69 is for anelectrical device in a system for allocating electrical resources, butthis usage should not be construed as limiting. The electrical devicecan be a variety of devices, such as an air-conditioning unit; heatingunit; heating, ventilation, and air conditioning (HVAC) system; hotwater heater; refrigerator; dish washer; washing machine; dryer; oven;microwave oven; pump; home lighting system; electrical charger, electricvehicle charger; or home electrical system. The method of FIG. 69 can beperformed using computing hardware (e.g., a computer processor or anintegrated circuit). For instance, the method can be performed by acomputer at an end-user's locale or home, a computer coupled to anelectrical device, or by specialized hardware (e.g., an ASIC orprogrammable logic device) coupled to the electrical device.Furthermore, it should be understood that the method acts in FIG. 69 donot necessarily occur in the illustrated sequence.

At 6910, a user comfort setting selected by a user is received. The usercomfort setting can be selected from at least a first user comfortsetting and a second user comfort setting, the first user comfortsetting indicating the user's willingness to pay more to achieve adesired performance level for an electrical device relative to thesecond user comfort setting. For example, any of the user comfortsettings shown in Table 7 or similar comfort setting can be received.The user comfort setting can be input, for example, through anappropriate graphical user interface displayed on a computer or througha keypad, touch screen, dial, or other control mechanism associated withthe electrical device.

At 6912, an average dispatched value is computed using multipledispatched values from a previous time period (e.g., a time period of 24hours or less). The multiple dispatched values can represent values atwhich electricity was dispatched by the market-based resource allocationsystem during the previous time period. The dispatched values can beperiodically received in order for this average value to be updated. Inalternative embodiments, a single dispatched value (e.g., the mostcurrent dispatched value) is received and used to perform the method. Instill other embodiments, a value other than the average value is derivedfrom the multiple dispatched values and used to perform the method(e.g., a median value, weighted sum, or other such derived value).

At 6914, a probability value of operating the electrical device iscomputed based on at least the user comfort setting and the averagedispatched value. In certain embodiments, a standard deviation of themultiple dispatched values from the previous time period is computed,and the probability value is additionally based on the standarddeviation.

At 6916, a determination is made as to whether to operate the electricaldevice using the probability value, and a signal is generated to causethe electrical device to operate based on this determination. In someimplementations, for instance, a random number is generated, which iscompared to the probability value. If the random number is less than (orin some embodiment greater than) the probability value, then the signalfor causing the electrical device to operate is generated.

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, or other such time period). In certain embodiments, some of thevalues are reused for subsequent time frames. For example, the usercomfort setting can be stored and reused for subsequent time frames.

H. Supply Strategies

Suppliers can consider many factors when computing their offer indexvalue. For example, if there is a production start-up cost, it can bespread over a minimum of M time frames using the formula:

$\begin{matrix}{{index} = {{variable} + \frac{startup}{M \cdot {capacity}}}} & (1)\end{matrix}$where index is the index value of the offer, variable is thetime-dependent index value (computed, for example, using the average ofdispatched indexes over N previous time frames as explained above andthe standard deviation of those dispatched indexes), startup is theindex value of starting production, and capacity is the total productioncapacity of the unit.

Similarly, a supplier for which production is already engaged can adjusttheir offer strategy by lowering their offer's index when they wish toassure their resource is used for a minimum number of time frames. Inorder to compensate for the potential lost returns during those minimumrun time periods, suppliers can increase their initial start-up indexenough to offset for potential losses. Here again, one approach is forresource producers to use the average and standard deviation ofpreviously dispatched indexes to forecast the most likely variations anduse an increment that minimizes the potential loss over the desiredminimum run time:

$\begin{matrix}{{index} = {{variable} - {M\frac{shutdown}{runtime}}}} & (2)\end{matrix}$where index is the offer index value, variable is the time-dependentindex value, shutdown is the index value of shutting down production,and runtime is the number of period over which the unit has already run.

Another common requirement for suppliers is that they not exceed amaximum production quota allotted for a number of time frames. Onesolution to this problem is to adjust the offer's index price based onhow much of the allotment has been used in relation to number of timeframes that have past. Producers that have used a disproportionatelyhigh allotment remaining will have lower offers than those that haveused a disproportionately low allotment remaining. For example, asupplier with a limited operating license can use:

$\begin{matrix}{{index} = {{variable} + {{{capacity} \cdot {fixed}}\frac{remaining}{{license} - {run}}}}} & (3)\end{matrix}$where index is the offer index value, fixed is the time-independentindex value, remaining is the number of time frames remaining unused inthe license, license is the number of time frames in the license, andrun is the number of time frames used in the license.

FIG. 70 is a flowchart 7000 showing a general embodiment for generatingoffer values for use in any of the disclosed recourse allocationsystems. The particular method shown in FIG. 70 is for an electricalresource (e.g., a generator) in a system for allocating electricalresources, but this usage should not be construed as limiting. Themethod of FIG. 70 can be performed using computing hardware (e.g., acomputer processor or an integrated circuit). For instance, the methodcan be performed by a computer at a supplier's locale, a computercoupled to an electrical generator, or by specialized hardware (e.g., anASIC or programmable logic device) coupled to the electrical generator.Furthermore, it should be understood that the method acts in FIG. 70 donot necessarily occur in the illustrated sequence.

At 7010, an offer value indicative of a value at which electricity canbe supplied by a generator for a current time frame is computed. In theillustrated embodiment, the offer value is based at least in part ondispatched values from previous time frames, the dispatched valuesrepresenting values at which electricity was dispatched by themarket-based resource allocation system during the previous time frames.In certain implementations, a standard deviation for the dispatchedvalues from the previous time frames is computed, and the offer indexvalue is additionally based at least in part on the standard deviation.The offer value can also be additionally based at least in part on astartup cost for supplying the electricity, a shutdown cost forsupplying the electricity, and/or a remaining number of time framesavailable in an operating license associated with the electricity (e.g.,using a weighted sum or other technique).

At 7012, the offer value is transmitted along with a value indicative ofa quantity of electricity that can be supplied by the generator duringthe current time frame to a central computer (e.g., using suitablecommunication means, such as the Internet or other network).

At 7014, a message is received from the central computer indicating adispatched value for the current time frame.

At 7016, the dispatched value is compared to the offer value, and thegenerator is activated in response to the comparison.

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, or other such time period). In certain embodiments, some of thevalues are reused for subsequent time frames. Furthermore, there areinstances when the offer value is used as a bid in the market-basedresource allocation system. For example, when the electrical resource isconfigured with an emergency transfer switch for supplying particularconsumers in the power grid, then the offer value can be used as a bidvalue along with a value indicative of a magnitude of electrical loadthe generator can remove from the power grid.

I. Ramp Rates

Some resources cannot change their production or consumption output morethan a certain amount within a single time frame. In this case, theresource being offered is not the quantity, but the change in quantity.This situation can be handled by treating the change in quantity as adistinct resource rather than an extra feature of an existing resource.This way, resources for which ramp rates apply have an extra resourceallocation strategy, which can be handled separately and independently.This strategy also helps maintain the independence of each resource asregards its allocation.

J. Multiple Resources

Each consumer and producer can engage in both demand and supply of anynumber of resources. For example, a producer may offer to supply aquantity of X at index A, while simultaneously requesting a quantity ofY at index B. If the producer depends on having Yin order to produce X,it address the risk of losing access to Y while still having to produceX by either adjusting the offer and request indexes, or ensuring that ithas an alternate source for Y or is ready to pay the penalty for notdelivering X. The same considerations apply for consumers.

K. Effect of Constraints

Frequently situations arise where a resource that is available in onepart of a service provider's system cannot be delivered in its entiretyto another part of the same system. Such delivery constraints can beaddressed by segregating the system into two separate resourceallocation systems that operate independently. For example, the systemwith surplus resources can make a supply offer into the system with adeficit, and the system with a deficit can make a consumption requestfrom the surplus system. Each system can dispatch its own index value,in which case the index difference will represent the impact of theconstraint on both systems. In some embodiments, the aggregator resourceallocation system can credit a capacity expansion account, which is usedto support the improvement of the connection between the two such thatthe constraint is eventually addressed.

III. Exemplary Implementations of the Resource Allocation Scheme andCase Studies Concerning the Exemplary Implementations

The following are examples of end-use and distributed resource controltechniques that can be used in the general resource allocation schemedescribed above in Section II and illustrated in FIG. 1. Thesetechniques have each been implemented and tested as part of anexperimental research project, which is described in much greater detailin Section IV.

A. Two-Way, Transactive Control for Thermostatically-ControlledEquipment

The techniques introduced in this section can be applied to the controlsof space heating and cooling (e.g., in residential or commercialbuildings). The approach can be extended to other contexts as well(e.g., the control of municipal water-pump loads, where the temperatureis replaced by reservoir height as the principal control input fordetermining the loads' bids).

In one exemplary embodiment, thermostatically controlled heating andcooling modifies conventional controls by explicitly using marketinformation obtained through interaction with a resource allocationsystem, such as any of the resource allocation systems introduced abovein Section II. In particular, the exemplary embodiment uses bid anddispatched index information. In the discussion below, bids anddispatched indexes are sometimes referred to in terms of a cost orprice. It is to be understood that this “cost” or “price” can representan actual monetary cost or price or a cost or price in terms of therelevant resource allocation index. Furthermore, the dispatched indexfrom the resource allocation system is sometimes referred to herein asthe “clearing price”.

A bid curve can be used to functionally relate the cost of a service toa user's comfort. FIG. 4 shows a graph 400 of an exemplary bid curvethat graphically illustrates several of the concepts embodied in thebidding technique described below. The exemplary bid curve in graph 400is derived from the mean and standard deviation of dispatched indexesover a 24-hour period along with exemplary minimum and maximumtemperature limits that result from a comfort setting selected by auser. Desirably, the standard deviation and average of the clearingprice will be continually evaluated and updated.

In the exemplary technique described below, the occupant of a locale orzone that is thermostatically controlled (the user, in this example)provides two inputs. First, the occupant selects a preferred temperaturesetting T_(set) for each scheduled occupancy period. For each occupancyperiod, the occupant also selects a comfort setting from among a set ofalternatives. In the exemplary embodiment, each comfort setting isassociated with pairings of elasticity factors and temperature limits(in the illustrated example: k_(T) _(—) _(L), T_(min) and k_(R) _(—)_(H), T_(max)). In certain embodiments, the pairings of comfort settingsand parameters shown in Table 7 or any combination thereof are used. Inother embodiments, parameters that deviate from those shown in Table 7but that are still based on variations from the standard deviation areused.

In the example illustrated by graph 400 in FIG. 4, the high-temperaturelimit T_(max) corresponds to k_(T) _(—) _(H) standard deviations fromthe mean price. Furthermore, in this example, the value k_(T) _(—) _(H)is automatically determined with the user's selected comfort setting(e.g., using a look-up table or file storing the elasticity factors andtime limits associated with each comfort setting). The values of k_(T)_(—) _(L) and k_(T) _(—) _(H) are not necessarily identical for theupper and lower parts of the bid curve. Further, in this example, ifboth k_(T) _(—) _(H) and k_(T) _(—) _(L) are sufficiently (orinfinitely) large, the thermostat will function like a normal thermostatunaffected by grid conditions and the behaviors of the market. With sucha configuration, the thermostat is said to be inelastic.

The example shown in FIG. 4 is one in which it is desired to cool thecurrent temperature (e.g., it is an example in which electricalresources are to be used for air conditioning). In the example, a highvalue of k_(T) _(—) _(H) will lead to relatively high bids when the zonetemperature exceeds the desired zone temperature T_(set), which a highbid will help make sure that the zone cooling bid will win the right tobecome satisfied. If k_(T) _(—) _(L) and k_(T) _(—) _(H) are small(representing elastic behavior), bids from the thermostaticallycontrolled load deviate little from the mean price, and currenttemperatures are permitted to vary throughout a relatively largetemperature range (from T_(min) to T_(max)) as the market's clearedprice changes.

Although the illustration assumes that the price of electricity ischanging in real time, transactive thermostat control can be adapted fortime-of-use, day-ahead, and critical peak pricing structures with minorand, in some cases, no modifications.

The following discussion describes one exemplary technique for computinga bid value for a next market interval. In the exemplary technique, thecurrent indoor zone temperature T_(current) can be determined and theconsequent bid price P_(bid) computed. According to the exemplarytechnique, the bid price is based on the slope of the bid curve and thedifference between the current zone temperature T_(current) and thedesired zone temperature set point T_(set). The corresponding bid pricedepends on additional parameters that are defined by the chosen comfortsetting and are indicative of the user's willingness to toleratedifferences in temperature from the desired temperature setting. In thisexample, the additional parameters comprise k_(T) _(—) _(L), k_(T) _(—)_(H), T_(max) and T_(min). The bid price P_(bid) is also based at leastin part on historical market clearing prices. For example, the bid priceP_(bid) in the exemplary technique is based at least in part on the meanmarket clearing price and standard deviation of the market clearingprices from the recent historical performance of the market. Forexample, the following equation can be used:

$\begin{matrix}{P_{bid} = {P_{average} + {\left( {T_{current} - T_{set}} \right)\frac{k_{T} \times \sigma}{{T_{limit} - T_{set}}}}}} & (4)\end{matrix}$where P_(bid) is the bid price, P_(average) is the mean price ofelectricity for the last 24-hour period (or other historical timeperiod), σ is the standard deviation of the electricity price for thesame period, and k_(T) and T_(limit) are chosen from k_(T) _(—) _(L),k_(T) _(—) _(H) and T_(max), depending on where T_(current) presentlyresides on the bid curve. For example, any combination or subcombinationof the parameters k_(T) _(—) _(L), k_(T) _(—) _(H) and T_(min), T_(max)and the comfort settings shown in Table 7 can be used.

In this example, the mean and standard deviation price parameters overthe prior 24-hour window were used. The intention of doing so was totrack the energy price trends closely without necessarily tracking thediurnal behaviors of that price. By using averaged price parameters, thetechnique is adaptive. Relative high and low price definitions are basedon recent historical information. Independent of any absolute pricethresholds, the exemplary technique can be similarly applied whereprices are high, where prices are low, and where the prices are tendingto increase. The use of a recent standard deviation enables thetechnique to automatically adapt and scale a bid value so that it iscompetitive in the present market while meeting the consumer's objectivewith respect to comfort and economy.

The resulting bid value can then be posted to the market (e.g., theresulting bid value can be transmitted to the resource allocation systemresponsible for determining the clearing price). The market thenestablishes the market clearing price using this and the other bids andoffers, as has been described in Section II. Depending on how theresource allocation system is implemented, the market can be clearedexternally (e.g., by a resource distributor that is external to theentity placing the bid) or internally (e.g., by a resource distributorthat is internal to the entity placing the bid). For instance, aresource allocation system according to the disclosed technology may beimplemented within a single building or system that is internallycontrolled (e.g., an HVAC system in a commercial building where eachtemperature zone is viewed as a separate consumer that can compute andpost bids). In this example of a resource allocation system that iscleared internally, the systems within a building compete for aninternal, rather than an external, reallocation of costs and services.

After receiving the resulting posted market clearing price, an adjustedzone set point T_(set,a) can be calculated. For example, in oneexemplary embodiment, the following equation can be used:

$\begin{matrix}{T_{{set},a} = {T_{set} + {\left( {P_{clear} - P_{average}} \right)\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma}}}} & (5)\end{matrix}$A graphical interpretation of the adjusted set point T_(set,a) is shownin graph 400 of FIG. 4. The thermostat's zone set point can be adjustedto the new adjusted zone set point. Once the set point is adjusted, thethermostat's conventional control takes over.

In graph 400 of FIG. 4, the adjusted cooling set point falls below thecurrent temperature T_(current), meaning that there presently exists anopportunity to cool the given space at an acceptable energy cost. Thisprocess continues for each market clearing cycle. The notion of a singlezone temperature set point no longer exists because the set point can beaffected by the market as well as a user's selected comfort setting.Note that T_(set,a) can be higher or lower than the desired set pointT_(set) based on the market clearing price. In cooling mode, loweringthe adjusted set point T_(set) below the desired set point will increasethe energy consumption as one takes advantage of low energy costs.

In general, transactive control can support more aggressive pre-coolingand pre-heating functions. (For example, lowering the set point belowwhat would normally be comfortable is done to pre-cool.) For somedynamic rate structures (e.g., time-of-use), the future price is known apriori; however, in the case of real-time pricing, the future price isunknown or highly uncertain. To pre-heat or pre-cool with real-timepricing, one should have the ability to forecast future prices. Incertain embodiments, pre-cooling and pre-heating comfort settings can beoffered.

B. Demand Response On/Off Control for Equipment with One-WayCommunication

This section describes techniques that can be used to control equipmentthat is not capable of computing and transmitting bids to the resourceallocation system but that nonetheless can benefit from adaptive controlstrategies. The exemplary techniques are described in the context ofcontrolling a water heater. The water heaters in this example differfrom the thermostatically controlled loads described in the previoussection in that they have no temperature measurement with which theycould formulate their present need for electricity as bids into amarket. Nonetheless, water heaters (or other electrical devices in whichonly one-way communication is possible, such as pool pumps, batterychargers, and the like) can be adapted to opportunistically respond tomarket prices without having to formulate and submit any bids.

According to one exemplary embodiment, a probability function can beused to control whether the electrical device should run during a giventime interval. For instance, the probability function can be used togrant the electrical device (e.g., the water heater) a probabilisticopportunity to run that is dependent on the relative magnitude of acleared market price.

For example, in one particular implementation, the basic water heatercontrols were modified so that the signal activating the water heaterwas interrupted with increasing likelihood as the clearing priceexceeded the reported historic average electricity price. The greaterthe difference between the clearing price and average, the more likelythe water heater circuit will be interrupted. In this implementation,the water heater user can select a comfort setting from among multiplepossible comfort settings. Each comfort setting is associated with aconsequent weighting factor (in the exemplary implementation, the k_(W)factor), which either attenuates or amplifies the effect of theprobability function.

A variety of probability functions can be used to control the waterheater, but in one particular embodiment, the following equation isused:

$\begin{matrix}{\begin{matrix}{r = {k_{w}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma}{\int_{- \infty}^{P_{clear}}{{\mathbb{e}}^{- \frac{{({\overset{\_}{P} - x})}^{2}}{2\sigma^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack}} \\{{= {k_{w}\left\lbrack {{N\left( {P_{clear},\overset{\_}{P},\sigma} \right)} - 0.5} \right\rbrack}};}\end{matrix}{r \geq 0}\;{{r = 0};{otherwise}}} & (6)\end{matrix}$where N is the cumulative normal distribution, and the factor k_(W) isdefined through the participant's selection of a comfort setting.

In certain embodiments, the probability parameter r can then be used totest the probability of turning the water heater off by comparing it toa uniformly generated random number between 0 and 1. For instance, if ris greater than this random number, the water heater can be curtailed.The random number can be generated at each time interval to give eachwater heater an opportunity to run a fraction of the overall time thatis proportional to the curtailment probability.

Table 1 shows the probability of water heater curtailment given variousvalues of comfort setting k_(W) according to one exemplary embodiment.Table 1 also shows the probability according to several exemplarycleared market prices. Specific values of cleared market price P_(clear)are provided for the case where the mean cleared market price over thepast day was $75/MWh, and its standard deviation was $25/MWh. As can beseen from Table 1, the likelihood of the water heater being turned offincreases with the cleared energy price P_(clear) and with the factork_(W). As can also be seen from the example in Table 1, the waterheaters are never curtailed when the price is below average. In otherwords, according to the example shown in Table 1, it is advantageous forthe water heaters to heat their water when the price is below average.

TABLE 1 Example of Water Heater Curtailment Probability for Values ofk_(W) (P_(mean) = 75, σ = 25) Multiples P_(clear) Factor k_(W) of σ($/MWh) 0 0.5 1.0 1.5 2.0 −3 0 0.0% 0.0% 0.0% 0.0% 0.0% −2 25 0.0% 0.0%0.0% 0.0% 0.0% −1 50 0.0% 0.0% 0.0% 0.0% 0.0% 0 75 0.0% 0.0% 0.0% 0.0%0.0% 1 100 0.0% 17.1% 34.1% 51.2% 68.3% 2 125 0.0% 23.9% 47.7% 71.6%95.4% 3 150 0.0% 24.9% 49.9% 74.8% 99.7%

C. Commercial Building Load Control

In this section, details of the experimental use of a market-basedcontrol technology used to control a building-space air conditioning andheating system are described. The experiment was implemented using anexisting Johnson Controls building automation system (BAS) with noadditional capital expenditure for making the commercial building moreenergy demand responsive. Instead, the building was made responsive tothe real-time electric energy market prices of a resource allocationsystem as described above. The elements that were controlled during theexperiment were the variable air volume (VAV) dampers servingtemperature zones within the building.

1. Traditional Building-Space Conditioning Controls

Most building control systems in large commercial buildings (e.g.,larger than 100,000 square feet) include HVAC systems that arecontrolled by a building automation system (BAS). A BAS has sensors tomeasure control variables (e.g., temperature and air flow rates), acontroller with the capability to perform logical operations and producecontrol outputs, and controlled devices that accept the control signalsand perform actions (e.g., dampers and valves). In addition, the BAS mayalso have a global supervisory controller to perform high-level tasks(e.g., resetting temperature set points based on building conditions andscheduling on and off times).

BAS technology has evolved over the past 3 decades from pneumatic andmechanical devices to direct digital controls (DDCs) or computer-basedcontrollers and systems. A BAS typically comprises electronic deviceswith microprocessors and communication capabilities. The widespread useof powerful, low-cost microprocessors and standard cabling as well asthe adoption of standard communication protocols (such as BACnet™ andLonWorks™) have led to improved BAS. Most modern BASs have powerfulmicroprocessors in the field panels and controllers that may soon beembedded in sensors as well. Therefore, in addition to providing betterfunctionality at lower cost, these BASs also distribute the processingand control functions to the field panels and controllers without havingto rely on a central supervisory controller for all functions.

In a conventional (or non-transactive) control application, shown inblock diagram 500 of FIG. 5, the principal control elements are thesupply air temperature 510 (which is the controlled variable), adry-bulb temperature sensor 512, a controller 514 (which compares thesensed supply air temperature value with a fixed set point and uses thedifference between the two to generate an output signal), a controlleddevice 516 (which in this case is a cooling coil valve controlling thechilled water flow to the cooling coil), and a process plant 518 (whichin this case is the cooling coil and air stream).

As the supply air temperature changes, the difference between themeasured supply air and the supply set point temperature changes; thecontroller 514 uses the difference between the two values to generate anoutput signal that repositions the cooling coil valve 516. As the valveis repositioned, the supply air temperature changes, and eventually themeasured temperature and the supply set point will be nearly equal. Notethat the supply air temperature is the only controlled variable in aconventional control approach; the cost of providing comfort and theperformance of the component or the system are not part of thedecision-making process.

2. Transactive Building Control

The process by which commercial-building bids for thermostatic zonecontrol were computed and transmitted into the resource allocationsystem were presented above in Section III.A. Specifically, equations 4and 5 were used to compute the bid values for each temperature zone. Thepurpose of this section is to further describe transactive control as itapplies to commercial buildings and to differentiate such control fromtraditional commercial-building controls.

Transactive networks and agent-based systems present an opportunity toimplement strategies in which a degree of both local and globaloptimization is an inherent attribute of the strategy, achieved throughmarket-based competition for resources rather than explicitlyprogrammed. Such market-based competition can be implemented using anyof the resource allocation schemes described above in Section II.

The premise of transaction-based control is that interactions betweenvarious components in a complex energy system can be controlled bynegotiating substantially immediate and contingent contracts on aregular basis in lieu of or in addition to conventional command andcontrol. Each device is given the capability to negotiate deals with itspeers, suppliers, and customers to maximize revenues while minimizingcosts. This is best illustrated by an example.

A typical building might have one or more chillers that supply chilledwater on demand to multiple air handlers. If several air handlersrequire the full output of one chiller, and still another air handlersuddenly also requires cooling, traditional building control algorithmssimply start up another chiller to meet the demand, and the building'selectrical load increases accordingly.

A transaction-based building-control system behaves differently. Insteadof submitting an absolute demand for more chilled water, the air handlersubmits a bid for additional service from the chillers, increasing itsbid in proportion to its “need” (e.g., the divergence of the zone orsupply air temperature from its set point). The chiller controls,possibly having knowledge of the electric rate structure, can expressthe cost of service as the cost of the electricity needed to run theadditional chiller plus the incremental capacity demand charges, wheresuch charges might apply. If the zone served by this air handler justbegan to require cooling, its “need” is not yet very great, so it placesa relatively the low bid for service, and the additional chiller staysoff until its level of need and consequent bid increases.

Meanwhile, if another air handler satisfies its own need for cooling,the cost of chilled water immediately drops because a second chiller isno longer required, and the bid from the air handler awaiting serviceperhaps then exceeds the present price, and it receives the chilledwater it had requested. Alternatively, a peer-to-peer transaction cantake place in which an air handler with greater need for servicedisplaces (literally outbids) another whose thermostat is nearlysatisfied.

In this way, the transaction-based control system accomplishes severalthings. For instance, the transaction-based control system inherentlylimits demand by providing the most cost-effective service. In doing so,the system inherently prioritizes service to its more important needsbefore serving less important ones. Further, assuming that noair-handling unit (AHU) is willing to pay the additional cost of serviceto start the second chiller, the transaction-based control systemdecreases energy demand and consumption by preventing the operation ofan entire chiller to meet a small load, a condition where the systemwould operate inefficiently. Additionally, contract-based controlsinherently propagate cost impacts up and down through successivehierarchical levels of the system being controlled (in this example, achiller or a boiler that provides cooling or heating, an air handlerthat provides air circulation, and the zone). The impacts on the utilitybill, which are easily estimated for the chiller operation, are used asthe basis for expressing the costs of air handler and zone services.Using cost as a common denominator for control makes expression of whatis effectively a multi-level optimization much simpler to express thanan explicitly engineered solution would be. It allows controls to beexpressed in local, modular terms while accounting for their globalimpact on the entire system.

In effect, the engineering decision-making process can be subsumed by amarket value-based decision-making process that indirectly injectsglobal information conveyed by market activity into the localengineering parameters that govern the behavior of individual systemsover multiple time scales.

Many HVAC systems are controlled by thermostats. The desired temperatureis set by the customer, and the thermostat uses the current spacetemperature to control the air-flow damper positions or to turn thecompressor off, thereby satisfying the heating and cooling needs of thezone. In a conventional control system, indoor temperature and indoorset-point temperatures control the amount of heating and cooling to eachzone. However, in a transactive control system, in addition to theconventional inputs, the thermostat also uses price information to makecontrol decisions. Although much of the discussion so far has been forthermostatically controlled systems, transactive controls can be appliedto non-thermostatically controlled systems as well.

3. Case Study of Transactive Control

In this section, the actual implementation of a transactive controlstrategy is discussed. In particular, the HVAC system of a commercialbuilding in Sequim, Wash., was modified to operate using a resourceallocation system as described above. The building (the marine scienceslaboratory (or MSL) building) was a mixed-use commercial building withboth office and laboratory spaces. The perimeter of the buildingconsisted of office spaces, while the core consisted of laboratories.The building was served by a heat pump chiller and a boiler thatsupplemented the building's heating needs when the heat pump chiller wasnot able to meet the building's heating needs. The office and laboratoryspaces had independent HVAC systems. The office spaces were conditionedby a multi-zone VAV AHU. Each office was served by a VAV terminal boxthat was controlled by a zone thermostat. The VAV boxes also had areheat coil to provide heating as well as reheat. For the office spaces,the zone temperature set points were different for the heating andcooling periods and also for occupied (in this example, 6:30 AM to 5:30PM) and unoccupied (in this example, 5:30 PM to 6:30 AM) periods. Thetransactive control strategy was applied to 12 VAV systems serving theoffice spaces.

The transactive control strategy, described in the previous section, wasprogrammed at two levels in the BAS (zone level and building level). Thebidding and calculation of adjusted set point occurred at the zonelevel, each zone bidding independently of other zones. Theuser-specified parameters (in this example, the user's desiredtemperature and the comfort settings according to Table 7) were enteredfor each zone. In this case, the facilities operator specified a commonacceptable temperature range for controlled zones (65° F. to 80° F.) anda common comfort parameter (k_(T)=3) as well. A zone level override wasalso provided so that the user could override the transactive controlstrategy and fall back onto the building's prior conventional controlapproach. Another way to override the transactive control is to set thevalue of k_(T) very high (>10), which emulates conventional control.

Some aspects of the transactive control were implemented at the buildinglevel. The current market price, the mean price, and the standarddeviation, for example, were posted from an external source at thebuilding level. In addition, a building-level override was also providedfor use by the building manager. Unlike the zone-level override, theoverride at the building level superseded all transactive controls atall levels, including at the zone level.

Although electric power markets are generally cleared infrequently at anhourly interval, the real-time market created for this experiment (alsoreferred to as the “shadow market”) cleared every 5 minutes. The zonesdid not directly participate in and bid into the experiment's market butrather used the cleared market price to adjust their set points based onthe market price. VAV damper control cannot be directly correlated toenergy price as could be done, for example, for the operation of aboiler or HVAC units.

The communication between the shadow market and the BAS was mediatedthrough an object link and embedding (OLE) for process control (OPC)server. To compare the response of conventional controls withtransactive controls, the building was operated with conventionalcontrols on Tuesday and Thursday and with transactive controls onMonday, Wednesday, and Friday.

Graphs 600 and 602 of FIGS. 6A and 6B compare the response of a singlezone on two consecutive days with conventional and transactive control.The heating and cooling set points during occupied hours (6:30 AM to5:30 PM) for the zone with conventional control were 71° F. and 73° F.,respectively. As seen from graph 600 in FIG. 6A, the zone temperaturewith conventional control was between the two set points most of thetime during occupied hours. Unlike the conventional control, on the daywith transactive control, the heating and cooling set points were notconstants but changed in response to the market price signal, as isshown in graph 602 of FIG. 6B.

Graph 700 in FIG. 7 shows the corresponding bid prices, market prices,and mean price. In this transactive control application, the zonethermostat “bids” were zero when the zone set point was satisfied. (Forclarification, these bid prices were calculated and used by thethermostats even though the bids were not placed into the market.)

D. Water Pump Load Control

This section summarizes an experiment performed in which the control ofmunicipal water pump-load resources was modified to respond to aresource allocation system according to the disclosed technology. Inparticular, five 40-horsepower municipal pumps from two pump stations inpublic utility district #1 (PUD #1) in the Clallam County serviceterritory on the Olympic Peninsula, Wash., participated in theexperiment. This section describes the operational performance of thepumps, their automated bidding into the exemplary price market generatedfor the experiment, and the times and durations for which the pumps werecurtailed.

1. Transactive Market for Real-Time Energy Control

A local marginal price signal was designed for the experiment and wasused in conjunction with the Clallam County pumps to automaticallydetermine when the pump loads should operate. The operation of theproject's market was described above in Section I. The control ofmunicipal water pump loads is similar to the control of thethermostatically controlled loads described in the previous section, butwater-reservoir level replaced zone temperature as the principal inputvariable from which the loads' market bids were determined.

Each pump station automatically submitted a bid to run its pumps for thenext 5 minutes based upon measurements of the actual height of thereservoir at the pumping station. The pump stations bid high when theirreservoir's level became low and bid lower when the reservoir's levelwas acceptable. An unsuccessful bid automatically curtailed the pumps'operation. Initially, the pumps' bids were not submitted to influencethe market, but rather the operation of the pumps was based on acomparison of the pump bid and market clearing price. If the bid pricewas greater than the market clearing price, the pumps operated normally;and if the bid price was less than the market clearing price, the pumpswere curtailed. During the experiment, the pumps also began bidding intoand influencing the market as a responsive load resource where theirbids reflected their reservoir water height.

2. Pump Load Control and Communications

The responsive municipal water pump load consisted altogether of five40-hp municipal water pumps, which were to maintain the level of waterstored in two nearby water reservoirs. These load resources were madeavailable for the experiment by PUD #1 of Clallam County, in whoseservice territory the pumps resided on the Olympic Peninsula, Wash.

Image 800 of FIG. 8 shows the interior of the Sekiu pump house and itstwo 40-hp pumps, and image 900 of FIG. 9 shows the corresponding waterreservoir. Image 1000 of FIG. 10 shows the exterior of the Sekiu pumpstation. Similar pumps and a reservoir existed at the Hoko River pumpstation and also in the Clallam County PUD service territory nearClallam Bay on the Olympic Peninsula.

Before the experiment, these sites used a simple control strategy tomaintain the levels of the reservoirs. Pumps were consecutively directedto turn on by their controllers at absolute water-height thresholds asthe water level in the reservoirs diminished. For example, the firstwould turn on when the reservoir dropped 1 foot. A second would come onafter the reservoir level dropped 2 feet, and so on.

For the experiment, controller switches were placed in series with theexisting controls. Therefore, the grid benefits by removing pump loadthat was already, or would be, part of the total system load. In otherwords, the experiment allowed for the pump operation to be curtailed,but not initiated. The main control panel at the Sekiu pump station isshown in image 1100 of FIG. 11. The Johnson Control (JCI) panel wasplaced into the pump house to control the pumps. The inside of one ofone such JCI controller can be seen in image 1200 of FIG. 12.

Block diagram 1300 of FIG. 13 shows a schematic diagram of theexperiment's control communications for controlling the pumps. Startingfrom the right-hand side of this figure, the experiment involved addinga JCI controller at each pump station to interact with the existing pumpcontrols. The JCI controllers communicated their status via a modem toanother controller (located at the Richland Pacific Northwest NationalLaboratory facility) via a similar modem and JCI box. The pump statuswas converted to a bid and capacity by the controller, which was thenrelayed to the servers located in the PNNL Electrical InfrastructureOperations Center (EIOC).

There, the bids from all loads and resources were received, and theproject's market cleared, resulting in a total regional capacity thatcan be supplied and consumed at the cleared market price. The resultingcleared market price was an input for the decision for each pump tooperate or not. This control-action signal was then relayed back to eachpump station and its controllers via modem.

3. Detailed Method of Bidding

PUD #1 of Clallam County offered the project the privilege to controlthe Sekiu and Hoko River reservoir levels within the ranges of 20 to 24feet and 12.5 to 15 feet, respectively (see Table 2). There were three40-Hp pumps at Hoko and two 40-Hp pumps at Sekiu. Before projectcontrol, each pump was configured simply to turn on at a reservoir leveland turn off at another. The turn-on pump levels were staggered suchthat more pumps would be used at decreasingly lower reservoir levelsuntil all the pumps at the site would be on.

The pump bid curve is shown in graph 1400 of FIG. 14 for Sekiu(asserting a mean price for the past 24-hours to be zero and standarddeviation of 1.0). The Hoko River pumps were controlled similarly withintheir allowable reservoir limits. The bid curve was later modifiedslightly to be more aggressive when the pump operators were present inthe pump station, 7 to 9 AM.

TABLE 2 Water Pump Control Prior to Project Involvement Condition HokoWater Level (ft.) Sekiu Water Level (ft.) all pumps off 15 24 first pumpon 13.5 23 second pump on 12.5 20 third pump on 11 NA low water alarm 1019

FIG. 14 is a graph 1400 showing the bid price for the Sekiu pumps as anumber of standard deviations above or below an average bid price whenthe pumps were controlled according to an embodiment of the disclosedtechnology. As can be seen in graph 1400, the Sekiu pumps bid an averageprice when the reservoir was at a 19-ft level. The pumps bid more as thereservoir level decreased. The pump operator could control both thepoint at which an average bid is asserted and the slope of the line,which represents the change in bid as a function of change in reservoirlevel. The likelihood of having a market price more than 3 standarddeviations away from the average price is very small. The control wasimplemented in series with the existing control loop, so project controlcould only turn off the pump; it could not turn the pump on.Furthermore, the project implemented software overrides that allowed thepump to run without risks of project load curtailments at some minimumreservoir level, regardless of the market's prices and bids. Reservoiroperators were also provided a means to override project control. Theseprecautions were taken to assure the PUD and its staff that they wouldalways maintain adequate emergency water reserves.

4. Pump Load Market Behavior

Representative operational data for the Sekiu pumps and reservoir levelwere plotted for a single day and are shown in graphs 1500, 1600, and1700 of FIGS. 15, 16, and 17. Graph 1500 shows the reservoir levelduring the test day, graph 1600 shows the pump bids for the test day,and graph 1700 shows the number of pumps on the test day. In graph 1500,the reservoir level is shown to be gradually recovering through about6:00 PM that evening. According to graph 1600 of FIG. 16, the pumps'bids reflect this fact, starting at a maximum bid and biddingprogressively less as the reservoir level recovers. The cleared marketprices were low except during the peak load morning period, as wastypical for the project's winter market. The cleared market priceexhibits several periods during which market information was notsuccessfully communicated throughout the system.

In graph 1700 of FIG. 17, pump #1 is shown to become briefly curtailedat each of the three market price spikes, where the market priceexceeded the pump bids, as is shown by the yellow pump status line(on=high state; off=low state). The pump bids and pumps appeared to beproperly responding. Their responses appropriately corresponded tochanges in reservoir water level and the relative magnitudes of pumpbids and cleared market prices, as designed.

As part of the pump control experiment, delay counters were applied bythe control algorithm upon the startup and shutdown of any pump. Thesecounters prevented the pumps from cycling on and off more quickly thandesired. The project originally set these delays at 10 minutes, but thisduration was found to allow the reservoir level to fall too low whilethe startup of a pump remained delayed and locked out. Satisfactoryperformance was achieved upon reducing the control delays to 5 minutes.

In the pump control experiment, the pumps communicated using phonelines. Because the market was clearing every 5 minutes, phone linesremained connected at all times (thereby increasing the cost ofcommunication). If the market clearing were to have occurred afterlonger period (e.g., every hour), the need for continuous communicationcould have been eliminated.

E. Distributed-Generator Control

This section summarizes an experiment performed in which powergenerators in a distributed network were modified to generate bids forand be controlled by a resource allocation system according to thedisclosed technology. In the experiment, two diesel generators werecontrolled. The two generators were a 600-kW Caterpillar dieselgenerator (the “upper” generator) and a 175-kW Kohler diesel generator(the “lower” or “beach” generator). Both generators were connected toassociated buildings and isolated from the grid using an automatictransfer switch. These switches were configured to automatically startup the generators whenever grid power became unavailable. The larger,upper generator served a critical main office and laboratory building;the lower, a smaller research building near the beach. These generatorswere appropriately sized to supply their entire loads isolated orislanded from the local power grid. The upper and lower distributedgenerators used in the experiment are shown in images 1800 and 1900shown in FIGS. 18 and 19, respectively.

The generators already possessed automatic-transfer-switch controllersthat were useful to the project. These controllers also communicatedwith the existing BAS. The existing field controllers (JCI controllers)were readily modified through software to participate in the localmarginal price market. Few hardware improvements were needed or made.

If the generators had been made operable in parallel with the powergrid, they could have supplied their entire nameplate capacity into thepower grid. However, with their emergency-transfer-switch configurations(comprising the hardware shown in image 2000 of FIG. 20), their value tothe power grid was the magnitude of load that they would remove from thepower grid whenever their emergency-transfer switch becomes activated.Therefore, these generators bid on the demand side of the market, notthe supply side, using the present magnitude of load they could removefrom the power grid with a successful bid. More generally, generatorsthat operate asynchronously with the power grid (e.g., generators thatcannot feed the grid, but can support a building, electrical device, orcustomer premises off-grid) can compute and transmit a bid correspondingto the load of the building, device, or premises that it supportsinstead of the generator's capacity. Generators that operatesynchronously with the power grid, by contrast, can bid their capacity.

1. Generator Bid Strategy

Each generator in the experiment prepared and submitted bids based onrealistic estimates of fuel and maintenance costs. These bids weretransferred via the Internet to another BAS at then to an ElectricalInfrastructure Operations Center (EIOC). It was in the EIOC that thebids from all resources and loads were gathered and resolved. Theproject market cleared every 5 minutes at some power magnitude andprice. The cleared price was then sent from the EIOC back to thedistributed generators, which compared their bid to the resultant priceand reacted by turning on or off.

Persistent communications were required to control the generators inthis way. The complete communication pathways are represented by blockdiagram 2100 of FIG. 21. Also shown in block diagram 2100 of FIG. 21 isa small 30-kW microturbine that was also included in the distributedpower system. This microturbine was also responsive to the two-waymarket. Unlike the larger generators described above, however, it ran inparallel with the power grid.

2. Detailed Method of Bidding

These definitions are useful for the discussion of how the distributedgenerator bids in the experiment described in the previous section werecalculated:

-   -   operating license—the total number of hours that the unit was        licensed to operate during a year. The typical period starts on        January 1, and the default license is 200 hours.    -   maximum daily runtime—the maximum number of hours that the unit        is permitted to run per day. The default was 4 hours per day.    -   maximum daily starts—the maximum number of starts that the unit        is permitted per day. The default was 2 starts per day.    -   current bid price—the price at which the unit will start. A NULL        bid indicated that the unit was unavailable.

The experiment used the following exemplary equation for computing thegenerator bid:bid=licence premium·(fuelcost+O&M cost+startup cost+shutdownpenalty),  (7)where

-   -   bid=generator bid price normalized for 1 hour of operation    -   fuel cost=variable cost of running for 1 hour    -   O&M cost=operating and maintenance cost per capacity for the        time allowed by the license period    -   startup cost=the projected penalties or costs for starting up        the units    -   shutdown cost=the projected penalties or costs for prematurely        shutting down the units.

The license premium factor in Equation (7) is used to modify the bids inlight of remaining unused licensed hours for the generator. One of thelargest constraints placed on diesel generators is the limit that isplaced on their operation for environmental reasons. Installed dieselgenerators have been limited to far fewer hours of operation per yearthan they might be run economically by the project's control system.Although negotiated for each unique installation, the number ofallowable runtime hours is in the 100- to 500-hour range. There was noenvironmental restriction on the experiment's microturbine, which usednatural gas as its fuel source.

Exemplary methods for computing the individual components of Equation(7) are introduced in the follow paragraphs. These methods should not beconstrued as limiting, however, as a variety of methods exist forcomputing such factors.

Fuel Cost.

This term can be represented as the product of the fuel cost and theconversion efficiency. This and the following terms can be normalized inthe sense that they become expressed as costs per hour of operation,which can then be directly used where market prices are expressed in theunits $/MWh.fuel cost ($/MWh)=fuel cost ($/gal.)×conv. eff. (gal./MWh)  (8)

Operations and Maintenance (O&M) Cost Per Hour.

This term can include both fixed and variable expenses. The O&M cost isdesirably spread out over the total run hours permitted for the unit torun in a year. For example, if one were to run the generator for itsentire number of licensed hours (hours the project can legally operatethe unit), the operations and maintenance cost would be normalized bythe licensed hours. In addition, the dollar per hour cost must benormalized by the capacity bid.

$\begin{matrix}{{{O\&}M\mspace{14mu}{cost}\mspace{14mu}\left( {\$\text{/}{MWh}} \right)} = \frac{{{{total}\mspace{14mu} O}\&}M\mspace{14mu}{cost}\mspace{14mu}(\$)}{{licensed}\mspace{14mu}{hours}\mspace{14mu}(h) \times {capacity}\mspace{14mu}{bid}\mspace{14mu}({MW})}} & (9)\end{matrix}$

License Usage Premium.

Since the generators had only a limited number of hours they could runduring a calendar year, a factor was defined to manage and allocatethese hours. This portion of the bid applies irrespective of the currentgenerator status (whether on or off).

$\begin{matrix}{{{license}\mspace{14mu}{usage}\mspace{14mu}{premium}} = {{scaling}\mspace{14mu}{factor} \times \frac{\left( {N - n} \right)}{N} \times \frac{M}{\left( {M - m} \right)}}} & (10)\end{matrix}$where N is the total number of hours in which to use the licensed hours.If the licensed hours are to be calculated for a calendar year, N willbe 8760 hours, and n will be the current hour of the year. M is thetotal number of license hours, and m is the number of licensed hoursalready used to date. The scaling factor is an arbitrary number lessthan 1. This term becomes infinite when the number of licensed runtimehours has become depleted; the term approaches zero toward the end ofthe license period.

Startup Cost.

This cost can be assessed to cover any startup costs that are incurredeach time the generator starts. In most cases, it is a fixed-dollaramount. The dollar cost is desirably normalized by the capacity bid andselected arbitrary time interval to recover the cost during thegeneration period. This cost only applies when the unit is off and isbidding its willingness to start. If the unit is already on, thisportion of the bid is zero.

$\begin{matrix}{{{startup}\mspace{14mu}{cost}\mspace{14mu}\left( {\$\text{/}{MWh}} \right)} = \frac{{startup}\mspace{14mu}{cost}\mspace{14mu}(\$)}{{capacity}\mspace{14mu}{bid}\mspace{14mu}({MW}) \times 1\mspace{14mu}{hour}}} & (11)\end{matrix}$

Early Shutdown Penalty.

This cost can be used to recover expenses for an early shutdown, which,if permitted, might cause excessive wear and tear on the generatorasset. In certain implementations, this cost will apply only if the bidinterval is less than a minimum threshold for the generator (e.g., 30minutes). If the bid interval is greater than the minimum threshold,there should be no early shutdown penalty. Also, if the unit is alreadyrunning, then this portion of the bid is zero. If the bid interval isless than the minimum threshold of operation and the generator is off,the normalized early shutdown penalty is:

$\begin{matrix}{{{early}\mspace{14mu}{shutdown}\mspace{14mu}{penalty}\mspace{14mu}\left( {\$\text{/}{MWh}} \right)} = \frac{{early}\mspace{14mu}{shutdown}\mspace{14mu}{penalty}\mspace{14mu}(\$)}{{capacity}\mspace{14mu}{bid}\mspace{14mu}({MW}) \times 1\mspace{14mu}{hour}}} & (12)\end{matrix}$

Table 3 shows the values of these variables used in the previouslypresented exemplary equation for estimating the bid prices of thegenerators.

TABLE 3 Variables Used to Calculate Distributed Generator Offers 600-kW175-kW 30-kW Variable Generator Generator Microturbine fuel cost ($/gal)2.80 2.80 12 ($/MMBtu) effic. (kWh/gal) 12.80 13.60 69.0 ($/MMBtu) bidinterval (min.) 5 5 5 O&M cost ($/year) 3,000 1,000 1,000 scaling factor0.2 0.2 0.2 N (hrs) 8,760 8,760 no limit M (hrs) 200 200 no limitstart-up cost ($) 10.0 5.0 0.0 capacity bid (kW) varies varies 30 kWearly shut-down cost ($) 50.0 10.0 5.0 minimum run time (min.) 30.0 30.015.0

Equation (7) should not be construed as limiting, as other equations ortechniques can be used to compute the generator bid. For instance, thegenerator bid could be based at least in part on any one or more of avalue indicative of the fuel cost, O&M cost, startup cost, shutdowncost, or cost associated with the operating license of the generator.

3. Observations of Distributed-Generator Market Behavior

Most of the time, the experiment's generators bid unfavorably—toohigh—as they competed with the existing, relatively low-priced poweravailable from the electrical distribution and transmission system.However, when the amount of power that could be safely received into theregion through its existing distribution and transmission was exceeded,prices rose. Bids from the additional resources were eventuallyaccepted, resulting in the activation of the additional generators. Inthis fashion, resources were equitably distributed in response to theelasticity of demand.

In this section, the behaviors observed for the experiment's real andvirtual generators as they participated in and responded to the localmarginal energy price market are summarized. The virtual generators wereused to simulate additional generators participating in the system, thusmaking the experiment more realistic of a wide-scale implementation ofthe system. The generators did not participate much in the market untila cooler time period, when space-heating loads in the region accompaniedlower morning temperatures. Approximate total recorded run times foreach generator, representing how long they ran on behalf of the project,were as follows: (1) APEL microturbine: 59 hours; (2) lower generator:65 hours; and (3) upper generator: 48 hours.

It was decided early in the project to assert that the diesel generatorsshould not be permitted to cycle on and off rapidly. Doing so mightadversely affect their lifetimes. Therefore, the generators wereprogrammed to bid very low (on the load side) for several market cyclesafter they began to run to ensure that they remained on for at least 30minutes once started. The effect of this and the startup cost premiumwas to create some hysteresis in their bids and prevent short cycling ofthe generators.

Two types of figures are presented below to demonstrate the operation ofthe diesel generators in the energy market—distributions of bid pricesand bid capacities. Graph 2200 of FIG. 22 shows the market closing pricewhen the generator bid was accepted during the test period for the twodiesel generators. The microturbine (not shown) bid a constant price ofabout 377 $/MWh because it was an energy supplier in the market, capableof running in parallel with the grid. The accepted bids for bothgenerators were in the range of about 180 $/MWh to 680 $/MWh, althoughthe 175-kW “lower” generator usually bid lower than the 600-kWgenerator.

The small gas microturbine, because of its lower bids, operated moreoften than the other two diesel generators. Also, the smaller 175-kW MSLdiesel generator was earlier to participate in the market and becameexercised before its larger neighbor. As a smaller generator, its bidand startup costs were lower on a per-kilowatt basis than those for the600-kW diesel generator. Although not shown here, most of thedistributed-generator activity occurred during early morning hours oncold mornings when feeder space-heating loads were high.

Graph 2300 of FIG. 23 shows the distribution of the capacity bids forthe two diesel generators. The 600-kW “upper” generator bid between 110kW and 390 kW while the smaller generator bid between 30 kW and 90 kW.The microturbine (not shown) was a 30-kW turbine that operated inparallel with the grid. It therefore provided a constant, predictableresource each time it was activated. Such is not the case for the MSLSequim generators, for which the project used existing automatictransfer switches to island the generators and their loads. These twogenerators bid the value of the building current loads that they couldserve on behalf of the power grid. The average hourly electric loads ofthe two MSL facilities that are served by, and determined the capacitybid magnitudes of, the two MSL generators are shown in graph 2400 ofFIG. 24.

The 600-kW upper generator unit was observed to have considerablevariability in the amount of capacity it bid, even during the same dayor operating period. The load in the served office and laboratory spaceshould not be expected to correlate perfectly with utility peak loads.Fortunately, the operation of the generator was shown to relieve atleast 110 kW of load from the grid at the times it was called upon. Thelower generator's bid capacity was both lower and exhibited a smallerrange.

4. Conclusions Concerning Distributed-Generator Resources

The two-way market approach successfully controlled the project'sdistributed-generation resources. Generators were assigned to run by themarket only when they were needed. The bids of the generators, inasmuchas it was possible, reflected actual and reasonable costs that would beincurred for fuel, maintenance, and other costs for the startup andoperation of the generators. The cost of configuring and controllingthese generators was moderate, taking advantage of existing automatictransfer switch hardware at the site. The value of the generators to thepower grid was their capability to island and remove a dedicated loadfrom the power grid.

F. Transactive Control System Bid/Response Strategy for Electric VehicleChargers

This section introduces exemplary methods for controlling chargers(e.g., electric vehicle chargers) in a resource allocation systemaccording to the disclosed technology. For instance, some of thedisclosed methods can be used in a two-way communication system, wherethe computing hardware associated with the charger generates bids fortransmission to the resource allocation. Other methods can be used in aone-way communication system, where the computing hardware associatedwith the charger responds to market prices and selectively activates anddeactivates the charger.

In certain embodiments of the disclosed technology, electric vehiclecharges (a) increase or decrease their bids in accordance with a user'scomfort economy setting (e.g., a user-selected value, referred to hereinas the k-value) in relation to the state-of-charge (SOC) and timeremaining to desire full-charge, and/or (b) reduce or increase therate-of-charge (ROC) based on the price cleared from the market.

In one exemplary implementation, the active bid strategy for an electricvehicle charger is based on the SOC, and the bid price is computed usingthe following:P _(bid) =P _(avg) −kP _(std) SOC _(dev)  (13)where P_(avg) is the average daily clearing price of energy, P_(std) isthe daily standard deviation of price, and SOC_(dev) is the fractionaldeviation of the SOC from a desired SOC(SOC_(des)) with respect tominimum and maximum limits (SOC_(min) and SOC_(max)) set by the user(e.g., SOC_(dev)=3(SOC_(des)−SOC_(obs))/(SOC_(des)−SOC_(max)) orSOC_(dev)=3(SOC_(des)−SOC_(obs))/(SOC_(min)−SOC_(des))). In operation,and according to one exemplary embodiment, the charger can be controlledso that it is turned on when the clearing price P_(clear) is less thanor equal to P_(bid) and turned off when the clearing price exceeds thebid price.

In embodiments in which only one-way communication is possible andbidding is not possible, then a passive control strategy can be used.For example, in one particular embodiment, a strategy can be used thatalters the rate of charge as a function of price. One exemplarycomputation uses the following equation:ROC_(set)=ROC_(des)(1−kP _(dev))  (14)where ROC_(des) is the desired rate-of-charge, such that

${{ROC}_{des} = \frac{\left( {{SOC}_{final} - {SOC}_{obs}} \right)}{n_{hours}}},$k is the user's comfort economy setting, with 0<k<∞, P_(dev) is theprice deviation, such that

${P_{dev} = \frac{P_{now} - P_{avg}}{P_{std}}},$SOC_(final) is the final desired state-of-charge of the vehicle,SOC_(obs) is the current observed state-of-charge of the vehicle, andn_(hours) is the number of hours remaining before the SOC_(final) mustbe achieved.

IV. Additional Information About the Case Studies

This section includes additional information about the case studiesintroduced in the previous section. In particular, the case studies wereperformed as part of an experimental project termed the “OlympicPeninsula Project.”

A. Summary of the Olympic Peninsula Project

1. Purpose and Objectives

The purpose of the Olympic Peninsula Project was to create andinvestigate experimental implementations of the energy-pricing schemesdescribed above. One of the goals of the project was to insertintelligence into electric-grid components at the end-use, distribution,transmission and generation levels in order to improve both theelectrical and economic efficiencies within the electric power system.Specifically, the project tested whether automated two-way communicationbetween the grid and distributed resources could enable resources to bedispatched based on the energy and demand price signals that theyreceived. In this manner, conventionally passive loads and idledistributed generators could be transformed into elements of a diversesystem of grid resources that provide near real-time active grid controland a broad range of economic benefits. The project controlled theseresources to successfully manage the power flowing through a constrainedfeeder-distribution circuit for the duration of the project. In otherwords, the project tested whether it was possible to decrease the stresson the distribution system at times of peak demand by more activelyengaging typically passive resources-end use loads and idle distributedgeneration.

Some of the objectives of the project were to:

-   -   show that a common communications framework could enable the        economic dispatch of dispersed resources and integrate them to        provide multiple benefits;    -   gain an understanding of how these resources performed        individually and when interacting in near real time in order to        meet common grid-management objectives; and    -   evaluate economic rate and incentive structures that influence        customer participation and the distributed resources they offer.

2. Background

Certain aspects of the project can be better appreciated after a briefexplanation of the smart-grid concept known as the “GridWise concept”and a review of conventional electric utility pricing practices.

GridWise Concept.

The term GridWise describes various smart grid-management technologiesbased on real-time, electronic communication and intelligent devicesthat are expected to mature in the next several years. By enabling anoverall increase in asset utilization, these technologies should becapable of deferring and, in some cases, entirely preventing theconstruction of conventional power-grid infrastructure in step withanticipated future load growth.

The Olympic Peninsula was selected as a location for experimental casestudies for several reasons. For example, the Peninsula is presentlyserved by a capacity-constrained, radial transmission system. The areais experiencing significant population growth, and it already has beenprojected that power-transmission capacity in the region may beinadequate to supply demand during extremely cold winter conditions.

Utility Pricing Practices.

While fixed electric energy rates still predominate in the UnitedStates, price-responsive electricity markets have made inroads.Time-of-use rates, including critical peak rates, have been offered inCalifornia and elsewhere to move electricity consumption to periods whenthe system is not at its peak. Administering time-of-use rates requiresadvanced utility interval meters that can distinguish and monitorcustomers' electricity consumption during peak and off-peak periods.While programs with advanced notification and long time intervals do notmandate the use of automation, adoption of time-of-use rates has beenaccelerated somewhat by the availability of interval meters andcommunicating energy-management systems that can automate customers'responses. Advanced metering and communicating thermostat initiativesare other recent examples of equipment development programs that couldhasten the propagation of time-of-use pricing contracts.

To a lesser degree, real-time contracts also have been offered tocustomers, but these practices have often applied only to large customerloads using relatively long time intervals. The “real-time” prices arecommunicated up to a day ahead based on advanced markets. For retailelectricity sales, the state of available automation supports responsesto price intervals down to about 15 minutes. However, these interactionswould best be described as one-way, i.e., they do not feedback demandbids.

Organized markets for wholesale electricity exist today. The nature ofsuch markets varies greatly with the degree of deregulated marketstructure region by region. No organized market exists in the Northwestor the Southeast. A few large entities conduct bilateral agreements, andthe resulting wholesale market price is not available until the nextday.

The Project Market.

Against this background, the Olympic Peninsula Project was undertaken toevaluate further steps in realizing the value of transforming passiveend-use loads and distributed generation into active, market-drivenresources for power-grid management as well as the practicality ofreducing the market clearing time of this process to intervals as shortas 5 minutes.

The project's market was operated at a 5-minute interval to allow thecycling behavior of loads to contribute to load reduction and loadrecovery. The duty cycle of most appliances, even on peak, is usuallysufficiently diverse to allow a load-control signal, such as price, totake advantage of the fact that they turn on and off anyway. Byadjusting when and how long loads turn on or off, a great deal offlexibility can be achieved to the benefit of the entire system. Becausemuch of this appliance duty cycling behavior occurs with a frequencycomparable to a 5-minute time scale, it was judged necessary to make theproject market operate on a similar time scale to exploit thischaracteristic of load behavior.

3. Project Resources

The Olympic Peninsula Project included the following controllable assetsthat were enabled to respond to the project's energy price signals:

-   -   five 40-HP water pumps, distributed between two municipal        water-pumping stations, representing a nameplate total load of        about 150 kW. The electrical load these pumps placed on the grid        was bid into the market incrementally when water-reservoir        levels were above a designated height in a water reservoir.    -   two distributed diesel generators. These two generators (175-        and 600-kW) served the facility's electrical load when feeder        supply was insufficient. The biddable resource capacity in this        case was the removal of the building electric load (−170 kW)        removed from the grid by transferring it to these units. In        addition, a small 30-kW microturbine was set up to respond to        the two-way market. Unlike the larger generators, the        microturbine ran in parallel with the power grid. The market        prices offered for the supply of these generator units were        based on the actual fixed and variable expenses incurred.    -   residential demand response for electric water and space heating        provided by 112 homes using gateways that supported two-way        communications. This residential demand-response system allowed        current market prices to be presented to consumers and allowed        users to preprogram their automatic demand-response preferences.        The residential participants were evenly divided among three        types of utility price contracts (fixed, time-of-use, and        real-time) and a control group.

While all residential electricity was metered, only the appliances inprice-responsive homes (−75 kW) were controlled by the project.

Automation was provided by the project to monitor, and in some casescontrol, each of these resources. All participants and resourceoperators were provided means to temporarily disable or override projectcontrol of their loads or generators. In the cases of residentialthermostats and water heaters, appliance owners were provided a means toassign a degree of price responsiveness to their appliances from amonglists of 5 to 15 intuitively named comfort settings. In the cases ofcommercial and municipal resources, the degree of automatedprice-responsiveness was negotiated with each resource owner.

While not all resources could be co-located on one feeder for thisexperiment, the measurement and control of these resources wereconducted as if all resided on a common virtual feeder. Throughout theproject, these project resources were monitored online at PacificNorthwest National Laboratory (PNNL) using distributed energy resource(DER) dashboards, such as dashboard 2500 shown in FIG. 25. This examplewould show a grid operator how much of a resource is available and howmuch has already been dispatched. These dashboards allowed project staffto quickly assess the status of the system and its individual resourcecomponents. Visualization tools of this type can be useful for gridoperators to achieve the widespread adoption of distributed resources.

One of the elements of the project was a shadow market used to providethe incentive signals that encouraged operation of the project'sdistributed generation (DG) and demand-response resources to managelocal distribution congestion. The project created debit accounts withbalances of actual money that were used to cover the shadow marketelectricity savings earned by the residential customers. The amount ofcash they earned and received depended on whether they were operatingtheir household loads in a manner that was responsive to the needs ofthe grid. As these customers responded to price signals sent from theshadow market, the cash balances in their debit accounts were reduced ata rate commensurate with the shadow market's current energy prices forthe given market interval. If consumers reduced their consumption moreduring period of high prices, they would save money. The participantsgot to keep any money left in the account at the end of each quarter.The project received guidance from the Bonneville Power Administrationto recommend reasonable values for these incentives with limited projectbudget in mind. Participating homes' energy consumption histories werealso studied before the experiment to establish baseline expectations.

Built upon the region's Mid-Columbia (MIDC) wholesale electricity priceand responsive to the feeder's real-time load needs and supplyavailability, the project's local marginal price reflected the effectsof (1) the resources offered and needed at the wholesale level, (2) thefeeder's economical capacity, and (3) the true marginal price of thefeeder's marginal resources. Over time, the price also reflected theeffects of customer behaviors as the customers reconfigured theirautomated responses based on their perceptions of the market and theirchanging comfort needs.

4. Brief Discussion of Findings

This section previews some of the findings of the project, which arediscussed in greater detail below:

Distribution Constraint Managed.

One of the project's goals was to manage congestion on a feeder.Seasonally, the project imposed a new constraint on the energy thatcould be imported into the feeder from an external wholesale electricitysource. The project then controlled the imported capacity below thisconstraint for all but one 5-minute interval during the duration of theexperiment. Graph 2600 of FIG. 26 previews this result. On this curverepresenting the duration of feeder capacity, the feeder supply (the redline) has been limited successfully to 750 kW. Distributed generatorsprovided additional supply (up to about 350 kW at its peak) when needed(green line).

Market-Based Control Tested.

The project controlled both heating and cooling loads. Observation ofthe project's residential thermostatically controlled loads for thosehomes on real-time price contracts revealed a significant, surprisingshift in energy consumption. This shift is shown in graph 2700 of FIG.27. Space-conditioning loads served by the real-time price contractseffectively used energy in the very early morning hours when electricityis least expensive. This effect occurred during both constrained andunconstrained feeder conditions; however, it was more pronounced whenthe feeder was constrained. This result is remarkably similar to whatone would expect for pre-heating or pre-cooling, but these projectthermostats had no explicit prediction capability. It is the diurnalshape of the price signal itself that caused this outcome.

Peak Load Reduced.

As shown in graph 2600 of FIG. 26, the project's market also deferredand shifted peak load. Unlike time-of-use control, the project'sreal-time control operated exactly when needed and with the precisemagnitude needed. The magnitude of load reduction under real-time pricecontrol increases with the peak itself and with the degree to which thefeeder import is constrained. The project conservatively estimated thata 5 percent reduction in peak load was achieved under a 750-kWconstraint; 20 percent peak load reduction was easily obtained under a500-kW feeder constraint.

Internet-Based Control Tested.

The project implemented Internet control of its distributed resources.Bid and control interactions were communicated via the Internet.Residential thermostats, for example, modified their effectivetemperature setbacks through a combination of local and central controlcommunicated over the Internet. The project market itself was clearedcentrally every 5 minutes (though other intervals could have been used).While average project connectivity to these resources was at timessporadic, the resources almost always performed well in default modesuntil communications could become re-established.

Distributed Generation Served as a Valuable Resource.

The project was able to obtain a useful supply from distributed dieselgenerators. The project elected to control the generators at theirexisting emergency-transfer switches. The generators and their protectedfacilities therefore ran separated, or islanded, from the grid. Thesegenerators bid the capacity of the commercial building loads theyserved; the price they offered was based on actual fixed and variableexpenses they would incur by turning on and running. These resourceswere called upon and used many times during the project.

Graph 2800 of FIG. 28 shows the total distributed generator energy usedby the project accumulated by hour of day. The diesel generators wererestricted by their environmental licensing to operate no more thanabout 100 hours per year. This constraint was easily managed by imposingand managing a price premium applied to every market offer made by theseresources. Note that many such emergency backup generators lie unused inthe United States.

5. Conclusions

The Olympic Peninsula Project was a unique experiment that revealedpersistent, real-time benefits of the disclosed technology. Results fromthe project indicate that local marginal retail price signals, coupledwith communications and the market clearing process, can successfullymanage the bidding and dispatch of loads and account quite naturally forwholesale costs, distribution congestion, and customer needs.

Overall, the Olympic Peninsula Project indicated the viability ofnumerous aspects of the disclosed technology on a common feeder. Theproject was planned at a large enough scale to offer unambiguousevidence that resources could be bid into an electricity market toprovide, in principle, solutions for a constrained power-deliveryinfrastructure that did not involve constructing new poles and wires.While technological challenges were found and noted, the project foundno fundamental technological limitations that should prevent theapplication of these technologies again and at larger scale.

B. Introduction to the Olympic Peninsula Project

This section describes the planning, commissioning, and results of theOlympic Peninsula Project. In particular, this section providesbackground context to explain the rationale for the Olympic PeninsulaProject in addition to the project's objectives, participants, approach,and planning.

1. Background Information

Historically, power-supply infrastructure has been constructed to serveload, a purely passive element of the system. Today, informationtechnology has been developed to the point of allowing larger portionsof the demand-side infrastructure to function as an integrated systemelement. Thus, for the first time, distributed electric load can be madeto actively participate in grid control and protection functions as wellas real-time economic interactions. The collective application of theseinformation-based technologies to the U.S. power grid is the backbone ofthe GridWise concept. GridWise technologies are expected to allow morepower to be delivered through existing delivery infrastructure andreduce the rate and cost of future system expansion to accommodate loadgrowth. At the same time, these technologies will increase gridreliability by using the load resources on the customer's side of themeter to make the grid inherently more efficient, stable, andreconfigurable.

a. GridWise Implementation

The transformational nature and broad scope of the GridWise concept willrequire substantial experimental testing before widespread adoption canoccur. Such a profound transformation requires field testing beforewide-scale adoption to establish the worth of a variety of technologiesand reveal possible shortcomings in their implementation andintegration. This transformation enables the integration of a diversesuite of distributed resources. These are anticipated to function inconjunction with existing utility assets to produce an aggregate valuemuch greater than the sum of benefits provided by individual componentsor subsystems. Key aspects expected of the GridWise implementation arethat it will: (1) provide benefits at multiple levels of the system fromthe same distributed resources (i.e., generation and wholesale markets,transmission, and distribution); (2) integrate multiple types ofdistributed resources (e.g., distributed generation and demandresponse); and/or (3) use “real-time” communication of market-likeincentives to obtain cooperative, voluntary responses from customers.

It is unlikely that the concept will gain widespread acceptance bydemonstrating individual technologies separately and in isolation. Thus,demonstrating GridWise benefits requires experimentation and testing atthe integrated system level. It is also desirable to demonstrate how newbusiness models and regulatory solutions can overcome institutionalbarriers to a GridWise implementation. Stakeholders, such as electricityconsumers, utility service providers, public utility commissions, andother interested parties should be involved in testing thesepropositions, as well.

The GridWise concept has achieved, to date, a coalescence of interest onthe part of utilities, regulators, and power and information technologydevelopers. The Olympic Peninsula Project represents a significant andtangible experimental use of multiple technologies acting in concert toshow that aspects of the GridWise concept are both practical andachievable.

b. Project Focus

The Olympic Peninsula Project was undertaken to evaluate how industrial,commercial, and residential demand-response and backup generationresources can be dispatched through real-time communication of costinformation and the end-use value of electrical services. These valueswere based on an experimental “shadow” market, which reflected realisticwholesale costs and incentives to relieve transmission congestion.

c. Olympic Peninsula Project Rationale

Both the geographical topography and the particular electric-gridconfiguration on the Olympic Peninsula in Washington State contribute toits being a desirable location for investigating GridWise technologiesin the Pacific Northwest. The Olympic Peninsula is dominated by thecentrally located Olympic mountain range. This topography has forcedhuman settlement predominantly at lower altitudes within an area ranginga few miles inland from a lengthy coastline bounded by the Strait ofJuan de Fuca and the Pacific Ocean. The largest of several small citiesand towns situated in this area is Port Angeles with a population now inexcess of 20,000 (18,397 in the 2000 Census). The region is not heavilyindustrialized. However, the area's population is increasing quiterapidly, resulting in a projected load growth of more than 20 MW peryear.

Port Angeles is supplied by two 230-kV circuits forming theShelton-Fairmount connection supplied by the Olympia Substation on theBonneville Power Administration (BPA) grid. Power transmission tocommunities west of Port Angeles is achieved at lower voltages over along and essentially radial system. The principal threat to powerdelivery on the Olympic Peninsula is an outage of a major transmissionline to Olympia. If this were to occur under extra-heavy winter loadconditions, the Olympic Peninsula could experience voltage instabilityand even collapse.

BPA has studied options for reinforcing the Olympic Peninsulatransmission system for many years. Various system and institutionalconstraints have presented challenges to designing economicalreinforcement using conventional construction that will both supportload growth and maintain adequate supply reliability. Because of theunique circumstances of its configuration, load density and diversity,and service conditions, both the transmission and distribution (T&D)portions of the Olympic Peninsula power delivery system have become,from BPA's perspective, prime candidates for “non-wires” enhancementsolutions. This approach calls for offsetting future needs for new T&Dconstruction with more cost-effective alternative measures, includingdemand-side management and improved use of existing infrastructure.

In principle, GridWise benefits can be investigated anywhere on thegrid. However, the value of field experiments in areas where the grid iscurrently robust or less constrained might be appreciated only at anacademic level of interest. Siting a test bed where a real need foralternative supply solutions is already apparent amplifies the prospectthat any benefits may be clearly recognized and rapidly adopted. Theseconsiderations provided a strong incentive for selecting the OlympicPeninsula grid as a prime project site.

2. Project Objectives

Among the objectives of the Olympic Peninsula Project were to: (1)evaluate whether a common communications framework can enable economicdispatch of dispersed resources and integrate them to provide multiplebenefits; (2) gain an understanding of how these resources performindividually and when interacting in near real time to meet commongrid-management objectives; and (3) evaluate economic rate and incentivestructures and other socio-political issues that influence customerparticipation and the distributed resources they offer.

Some of the more specific desired outcomes of the project included: (1)evaluating how transmission and distribution capital investment can bedeferred; (2) evaluating the role that demand response will play in thefuture and its potential benefits in the residential and commercialsectors; (3) evaluating how distributed generators can contributebenefits to the system beyond the energy they produce; and (4)evaluating how distributed resources can enhance the stability andreliability of the system. Some additional objectives of the OlympicPeninsula Project were that it would: (1) help develop alternativesolutions to power-delivery problems with broad national applicability;(2) help achieve valuable GridWise research goals; (3) be able todisplay system benefits in quasi-real time using a compelling visualinterface; and (4) serve as an expandable platform to integrate diverse,geographically dispersed regional experimental efforts.

It is recognized that there is likely no single technological “silverbullet” that will verify the best, most cost-effective use of power gridassets. Rather, one must integrate a broad range of new, distributedresource technologies with existing grid assets. Achieving anappreciable level of technological integration is considered to be amongthe most challenging objectives of the Olympic Peninsula Project.

3. Approach

A range of dispersed supply-side and demand-side resources were deployedat various locations on the Olympic Peninsula transmission route. Theseresources were integrated into a virtual physical operating and marketenvironment, backed with real cash consequences that allowed a degreeand quality of experimentation previously unavailable to the GridWiseprogram. By linking and co-managing demand and distributed generators inthe same economic-dispatch system, their relative cost efficiencies,their degree of response, and the synergies between them were measuredas functions of time-of-day, day-of-week, time-of-year, and duration ofcurtailment.

a. Distributed Resources

The assets introduced in this project were deployed to complement andleverage BPA's investment in “non-wires” solutions that address thegrowing Olympic Peninsula transmission constraint. The following arebrief statements of how each of the distributed resources was deployed.

Distributed Generation and Demand Response at Marine Sciences Laboratory(MSL).

Pacific Northwest National Laboratory (PNNL) operates the MSL campus inSequim, Wash., which has two diesel backup generators, a 175-kW unit anda newer 600-kW unit. These two generators were integrated into themarket dispatch system of the Olympic Peninsula Project using theexisting Johnson Controls building energy-management system at the MSLand its automatic transfer switch. The project calculated and providedlocal marginal costs to these resources to modify their control based onprice signals from the shadow market. The distributed generators bidtheir actual costs for starting and running for short intervals,including their automated management of environmentally permitted runtimes.

Transactive Commercial Building Demand Response.

The office building of the MSL also responded to project market pricesignals using the transactive building control technology disclosedabove, in which thermostatically controlled zones within the buildingwere made to compete amongst themselves for limited energy resources.Each zone bid for the resource according to the variance between itstemperature set point and its actual zone temperature. The zones havingwinning bids were granted air flow through control of their variable airvolume (VAV) flow dampers.

Automated Residential Demand Response.

The Olympic Peninsula Project recruited 112 homes to installenergy-management systems that supported two-way communications. Thisallowed the project's current market prices to be distributed toresidents and provided a user-programmable automatic demand-responsecapability for residential water heaters and thermostatically-controlledheating, ventilation and air conditioning (HVAC) systems. End-use datacollection was also incorporated so that both automated and manualdemand response of various appliance loads could be measured for someresidential clothes dryers, water heaters, and HVAC systems. Thethermostat control provided setback demand-response conservationbenefits during both the cooling and heating seasons.

The practical realization of these residential demand-response resourcesapproached 1.5 kW per home, or about 160 kW in total. Experimentparticipants were encouraged to tailor and pre-program their desiredautomated demand responses via Web sites accessible from their homes'personal computers. Thereby, participants could select their ownpreferred balance between energy cost savings and comfort. The projectprovided participants educational materials concerning the programmingof such automated responses and the voluntary efforts they could pursueduring the project to achieve even greater benefits. Warning lights andvisible indicators alarmed during periods of high electricity prices atthermostats and at some clothes dryers.

In addition, 50 clothes dryers and 25 water heaters in these homes wereequipped with GFA underfrequency load-shedding capability. See, e.g.,Hammerstrom D. J. et al., “Pacific Northwest GridWise™ Testbed Projects:Part 2. Grid Friendly™ Appliance Project,” PNNL-17079, Pacific NorthwestNational Laboratory, Richland, Wash. (2007).

Advanced Process Engineering Laboratory (APEL) Microturbine DistributedGeneration.

The project also incorporated a 30-kW microturbine resource that hadalready been made remotely controllable through prior contract work forBPA. This generator represented the project's only paralleled generator,meaning it and the facility it served remained grid-connected while thegenerator unit ran. Because the startup costs incurred by themicroturbine were small, the microturbine was the most activedistributed generation resource used in the project.

Hoko River and Sekiu Municipal Water Pump Demand Response.

Public Utility District #1 of Clallam County water department workedclosely with the project to provide and observe control ofwater-reservoir levels at its Hoko River and Sekiu water pumpingstations. Control was implemented via Johnson Control systems at thesetwo sites to control a total of five 40-hp municipal water pumps. Thecontrol traded off small variations in allowed water-reservoir levelsfor the control of times during which pumps were allowed to run. Thiscontrol was made responsive to the shadow-market price-control signalsof the project.

Virtual Distributed Generation Resources.

Due to cost and time constraints, the project also incorporated virtualdistributed generator resources of various sizes into the shadow marketin addition to the real generators. The operation of the virtualdistributed generators was simulated, emulating the same controlobjectives and constraints applied to the project's real generators.Environmental restrictions applied to the virtual generators as for thereal ones.

b. Shadow Market

One of the main organizing elements of the experiment was to implement anear-real time shadow market to provide the incentive signals thatinduced operation of the project's distributed generators anddemand-response resources. The project integrated real resources into avirtual market that allowed the resources to compete and respond topricing signals.

To avoid potentially lengthy delays and regulatory hurdles that would beencountered designing special rates for customers and implementing themin actual utility billing systems, the project's shadow market created,in effect, a debit account that customers could earn by operatinghousehold appliances in collaboration with the needs of the grid.Residential electric customers were given real cash balances at thebeginning of each month. As these customers responded to price signalssent from the virtual market, their cash balances were reduced orremained unchanged, depending on the value of their demand responses.Quarterly, the project disbursed the remaining funds in these accountsto the participants. This virtual market environment, backed with realcash consequences for customers, allowed meaningful experimentation withvarious market constructs and price signals.

The project represented the first limited-scale experimental use of atwo-way clearing market with a short clearing interval (e.g., 5 minutes)at the retail level. For this experimental market to produce resultswith any validity, it was desirable that the price signals realisticallyreflect the structure and magnitude of price signals and rates designedto induce demand response in the future. This approach also allowed theincentive structures to be varied across customers or time to experimentwith their effect on customer response.

The shadow market system was set up to communicate the real-time (e.g.,5-minute) aggregate local marginal price for electricity to eachcustomer involved. These marginal prices included the costs forwholesale power in the Western Interconnection, as indicated by thebehaviors of the Mid-Columbia (MIDC) price, and market incentives torelieve the transmission constraint as were determined by the automatedresolution of load bids and supply offers in the market.

An Internet Scale Control System (iCS), a Web-Sphere™ based middlewaresoftware available from International Business Machines, was used as thefoundation of the shadow market system. The market features of thereal-time contract operations were carried out centrally, but thesefunctions were integrated by IBM's middleware into the project as if thefeatures had been provided locally at every home gateway. The softwareenabled the display of both incentive signals and resource responses innear-real time on the project Web site and allowed the project to browsehistorical results. The middleware software also allowed dynamicre-configuration of the system by adding or removing residential homecomponents as well as user preferences and settings. Block diagram 2900of FIG. 29 shows the principal elements of the communication system.

c. Distribution Benefits with a Virtual Feeder

The Olympic Peninsula Project was designed to indicate how peak loads ondistribution feeders can be managed to avoid the need for local capacityexpansion. To do this, the widely distributed real assets of the testbed were integrated into a virtual distribution environment where theyappear and perform as resources available on a singlecapacity-constrained feeder. The shadow market was employed to signalthese assets to operate and to manage this constraint as if they wereactually all co-located on such a virtual feeder.

This feeder's capacity constraint could be arbitrarily modified duringthe experiment to throttle the activity of the control imposed by theproject's market. Three different capacity constraints were assertedduring the project's duration.

C. Local Marginal Energy Price Market

An experimental local marginal price market was designed and implementedon the Olympic Peninsula. Additionally, residential, commercial, andmunicipal loads and distributed generators were identified and used tobid into and respond to the local marginal pricing market. This sectionsummarizes the design and operation of this market and how thedistributed resources were controlled by this market during theexperimental test period.

1. Introduction to Transactive Control

By transactive control (sometimes also referred to as “contract nets”),the project refers broadly to market-based building control systems,whether those systems are used locally within a single building orfacility or throughout a region. The project chose a two-way market inwhich both suppliers and loads submit bids. This approach is remarkablyscalable. It can be successfully applied within a building, as was donein this project to create a market competition between spaceconditioning zones, and it can be applied regionally as the project didat multiple residential and commercial building locations.

Experiment participants who participated in the real-time marketsubmitted demand price bids for the expected power to be used by themduring the next 5-minute interval. These bids were placed at the priceat which they would be willing to curtail the stated power consumption.Most consumers submitted at least two bids for each 5-minute interval,one for their controllable, curtailable load and the other for theiruncontrolled, non-curtailable load. Consumers' uncontrollable,uncurtailable load power was always bid at $9999-infinity from theperspective of the project's market.

The one generator that was able to run in parallel with the power gridalways submitted bids for the maximum nameplate generation capacity itcould supply. The price of its supply offer consisted of all costs thatwould be incurred to start the unit and included the effects of minimumallowable runtime and environmental permits. Minimum runtimes wereenforced by bidding a high start-up price, followed by very low runningprices for the first few 5-minute periods until the minimum runtimeexpired. The running price was then escalated until it met the steadyrun cost, usually within one-half hour. The complete formulas by whichgenerators automatically bid were presented above in Section III.E.2.

Backup generators that could not generate back into the distributionnetwork were required to bid as consumers. Thus, non-paralleled backupgenerators always bid on the demand side, not the supply side, of themarket. However, they could only bid the capacity of the load that theywere backing up at the time of the market clearing. The offer, however,was also calculated to reflect the effects of actual startup costs,runtime costs, minimum runtimes, and environmental constraints.

The retail market was cleared every 5 minutes. All demand bids andsupply offers were sorted by price while summing their cumulativecapacity, thus producing the demand and supply curves for that market.The intersection of the load and supply curves always occurred in onepoint, which was published back to all bidders as the market's clearingprice and cleared power quantity. If the curves did not intersect, suchas when the uncurtailable load quantity exceeded the sum of all supplybid quantity, then the market cleared at $9999 (infinity). This occurredonly once in more than 100,000 market clearings during this project,corresponding to a single 5-minute interval during which theunresponsive demand did indeed exceed all available supply.

2. Two-Sided Real-Time Market with Clearing

To help convey the system operation of the project's real-time energymarket, graph 3000 of FIG. 30 shows an example of a two-sided marketclearing diagram 3-day “snapshot” for the operation of the project. Theloads' price bids are arranged from highest to lowest as one proceedsrightward toward higher total cumulative load. The supply price bids areshown ascending in price with increasing cumulative supply.

Supply.

The extended, flat base price, leftmost on the supply curve, representsthe base price for energy that can be delivered by the constrainedfeeder. The simulated feeder constraint is shown arbitrarily assigned bythe project at 500 kW in graph 3000 (the location of the first step inthe supply curve). This much power is readily imported into the regionat a cost assigned equal to the bulk wholesale cost of electricity plusa small premium. The project chose to assign this wholesale cost byprojecting hourly MIDC wholesale price data from the prior day,according to data collected from Dow Jones. The projection of day-aheadprice was problematic only on Mondays and Sundays, for which day-aheadmarkets were unavailable. On these two week days, wholesale prices wereprojected without dynamics from known recent average and peak dailywholesale prices.

The higher priced plateaus toward the right of the supply curve are theoffers received from the project's real and virtual distributedgenerators. Due to cost and schedule constraints, with the exception ofa single 30-kW microturbine, these distributed generators on the supplycurve were simulated to emulate the market behaviors and performance ofreal distributed generators of various sizes.

Demand.

The “infinite” demand-side bids by the uncontrolled project loads(vertical line leftmost on the demand curve) represent all loads ofthose residents who were not assigned to the real-time price contractsand also the dishwashers, refrigerators, lighting, and other loads inreal-time contract homes that were not configured to bid into themarket. The next large steps usually corresponded to the offers of thereal and virtual emergency-backup diesel generators that bid thecapacities of the loads they served. The multiple small steps evenfarther to the right corresponded to the responsive pumping loads andresponsive residential loads in real-time contract homes.

Clearing Process.

The project's published local marginal price for each interval is theprice at which the load and supply curves intersected. The historic5-minute local marginal prices are displayed as green diamonds in graph3000 of FIG. 30. It can be seen that the recent history in this figureincludes higher prices when the transmission constraint would have beenexceeded and higher-priced distributed generation resources were startedto avoid exceeding the constraint. As shown at present, however, thosesmall loads bidding to the right of the intersection choose not tooperate, and total system load is being held at the feeder constraintcapacity (500 kW).

In general, if the total participants' demand was less than the feeder'scapacity, the retail price was the same as the wholesale price. When thefeeder's capacity was exceeded, the retail price would rise according tohow the retail market cleared. Those loads to the right of theintersection defer their operation also to help manage the constraint.However, all bidding loads share the responsibility and any discomfortsequitably over time because the automated bid process dynamicallyprioritizes the loads according to their present needs. The loads arequeued from highest bid to lowest. The highest bidding loads arepermitted to run; low-bidding loads compete unsuccessfully in the marketand do not operate. By using transactive control throughout theproject's region, a single local marginal price was sufficient to manageboth load and generation resources in the region.

It is also interesting to view this 3-day period in another way. Graph3100 of FIG. 31A shows the time history of loads and local marginalprices for the same 3-day period used in graph 3000 of FIG. 30. Thetotal cleared load (black line) is the sum of the unresponsive loads onthe system (i.e., things like household refrigeration and smallappliances that were not controlled by the project, the blue line) andthe controlled loads. When the total load approaches the feeder capacitylimit (horizontal red line), the local marginal price (the black line ofgraph 3102 of FIG. 31B) increases sharply and helps keep the dynamicsystem load below the limit.

On the supply side of the market, the higher clearing local marginalprices become enticing to generators, which eventually turn on toincrease the total allowable capacity of the region. The startup ofdistributed generators is concurrent with the instances where the totalcleared load significantly exceeds the transmission constraint.

3. Source and Load Bids

Having discussed an example of the system-wide, aggregate behaviors ofthe resources as they participated in the project's real-time market,the discussion now addresses the general methods by which the project'sresources calculated their bids and offers into this market. The generalapproaches can be organized as

-   -   bids and responses from thermostatically controlled loads    -   responses from non-thermostatically controlled, non-bidding        loads    -   distributed generator resource offers.

4. Transactive Control for Thermostatically-Controlled Equipment

The transactive control for thermostatically-controlled equipment wasdescribed above in Section III.A.

5. Water Heater Controls

The water heater control strategy that was utilized in the experimentwas described above in Section III.B.

D. Residential Load Control

The purpose of this section is to explain the Olympic PeninsulaProject's interaction with its residential participants and theresidential loads controlled by the project.

1. Project Locations

With assistance from BPA, the project identified opportunities andobtained permissions to recruit residential participants in and nearSequim and Port Angeles, Wash., on the Olympic Peninsula. These regionswere located in the two utility service territories operated by PUD #1of Clallam County and the City of Port Angeles. Several homes were alsorecruited in the service territory of Portland General Electric inGresham, Oreg. Eventually, 112 experiment participants were successfullyrecruited to participate in the Olympic Peninsula Project.

2. Recruitment Process

Potential participants for the project were recruited with theassistance of their local utility companies. The utilities targetedtheir recruitment efforts toward potential participants who would likelyhave high-speed Internet service, primarily electric HVAC space heatingand cooling, an electric water heater, and an electric dryer.

The project received mailing lists for these potential participants fromthe utility companies. Recruitment letters were mailed to potentialresidential participants on the Olympic Peninsula. The recruitmentletters listed the participation requirements and asked them to visit aproject Web site to provide the project their contact information andanswer a series of questions that were later reviewed to determine ifthe participant met the participation requirements. The projectintentionally over recruited at this point, knowing that some of theinvited applicants would become disqualified by later screening effortsto be conducted by the project's automated Web site and by follow-upphone interviews. Despite much effort expended to recruit 200participants, the project only located and successfully signed up 112qualified participants.

The project also encountered challenges understanding the nature ofspace-heating equipment in applicant's homes. The preference was forapplicants having HVAC systems for both the heating and cooling of theirhomes. Unfortunately, the cool Olympic Peninsula has relatively littleair conditioning load, and few suitable homes were found to have HVACsystems. However, the interaction of the project's home automationequipment was obtained through thermostats, so the project ultimatelyalso accepted applicants with all or part of their homes' heating loadsserved by resistive heating as long as the home was served by one, or atmost two, thermostats. The project attempted to clarify the types ofhome heating systems in the applicants' homes by inquiring how manythermostats controlled the homes.

3. Participant Qualifications

In this section, the residential-applicant qualities the project soughtare summarized. The qualities sought were:

-   -   The applicant must be served by one of the two participating        utilities. Two utilities had agreed to informally cooperate with        the project. Utilities accepted the responsibility to replace        participant revenue meters with the project's advanced meters.    -   The applicants had to own and occupy their own homes throughout        the project duration. The project required participants'        permissions to modify the homes' electrical service and to        attach control boxes with fasteners to walls. Unoccupied homes        were avoided because the project wished to test the interactions        of occupants with provided equipment.    -   The applicant must have HVAC space conditioning (later relaxed        to the applicant having not more than two central thermostats).        The project would interact with participating homes through one,        or at most two, thermostats. The project wished to affect both        heating and cooling loads.    -   The applicant must have at least one 30-gallon or larger        electric, not gas or solar, water heater. Project load-control        modules were to be installed between the electric water heaters        and 240-VAC service. Only water heaters with reservoirs have the        thermal energy storage that can be used for peak reduction in a        way that would be accepted by participants.    -   The applicant must receive and subscribe to broadband Internet        service. The project's home gateway communicated with the        project via broadband Internet connections. The project desired        to interact with applicants who would be savvy enough to use        their Internet user interfaces and to take project surveys        online.    -   Each applicant's revenue meter had to be within 60 feet of the        home. This criterion was adopted soon after installations began,        at which time the project learned this limitation of the premise        wireless communications. At that distance, the wireless signal        became unreliable.    -   Optionally, the applicant must use an electric, not gas, dryer.        At a sample of these homes, the project investigated the        broadcast of energy price information at the clothes dryers'        user interfaces.

4. Incentives

The project offered applicants (1) the use of project equipment for themanagement and monitoring of their home water heaters and space heatingand (2) a total of $150, on average, cash earnings, more or less,depending on the occupants' responses to the energy signals provided tothem by the project. These incentives were offered in the initialrecruitment letters to applicants and were carefully stated in thecontract between participants and the project. Participants earned morethan this amount due to an extension of the project one additionalquarter beyond what had been initially planned.

At the beginning of each project month, each participant's projectaccount was refilled with an amount of cash. The amount of cash wasunique for each participant, based on the participant's assignedcontract type and his/her historical consumption of electric energy athis/her home. During the month, cash was removed by the project fromeach account commensurate with the time and contract price at which eachparticipant consumed electricity. The remainder, if any, was returnedeach project quarter to the participant by check. Participants' monthlyaccounts were never allowed to become negative.

The participants' incentive accounts were unaffected by customers'normal electric utility bills. The incentive accounts only dealt withthe differential electricity costs and benefits that the customers wouldhave incurred had they truly been under contracts that might havediffered from the fixed-price contract offered by their local electricproviders.

Quarterly, the project reviewed these accounts and calculated and mailedthe project incentive checks. Some modifications were needed at thesetimes to target the promised average compensation. These modificationswere justified by the project because of limited project funds and thesignificant variability some of these contracts exhibited with seasonaltemperature variations.

5. Participant Obligations

Each participant became contractually obligated to (1) make reasonableallowances for access by the project into homes to install, fix, oruninstall project equipment, (2) take initial and final surveys providedthem by the project, and (3) occupy the home and interact with theprovided project equipment. Participants were required to inform theproject in advance if they would be unable to complete their projectparticipation for any reason.

6. Contract Types and the Assignments of Contract Types

Participants were offered three types of electricity contracts: fixed,time-of-use (TOU) with critical peak price (CPP), and real-time price(RTP). Participants requested and were assigned to these three contracttypes and a fourth experimental control group in roughly equal numbers.The experimental price contracts did not change the existing obligationsthe customers had with their existing electric service providers.

The following quoted educational information was provided to theparticipants concerning these contracts at the time they were asked tostate their first and second preferences:

“Fixed Price Program Contract—

This choice requires little or no involvement from you and little to nochange in your electricity usage patterns. You are most likely toreceive a small program payment with this contract choice.

“The price of electricity under the fixed-price program contract willremain constant, regardless of when you use electricity or how much youuse at any one time-just like the bill you currently receive from yourlocal utility. In this program contract, there is no incentive forchanging your usage when electricity is in short supply. However, youmay affect your program bill by using more or less electricity.

“Tips to minimize your program bill under this program contract:

“Perform energy-efficiency strategies to save energy, such as turningdown your thermostat, replacing incandescent light bulbs with compactfluorescent light bulbs, installing low-flow showerheads, switching fromwarm to cold water cycles in the washing machine, installing stormwindows, checking and installing sealing and weather stripping, etc.”

The project's fixed price of $81/MWh (8.1¢/kWh) was selected and usedfor these contract participants. This price was determined by BPAproject collaborators based on a forward market price for a comparablysized load, plus a small service provider markup.

Following is the invitation for applicants to participate in theproject's time-of-use contracts:

“Time-of-Use/Critical Peak Program Contract—

This program contract choice will require a moderate level of consumerinvolvement and change in your electricity usage patterns. You are mostlikely to receive a moderate program payment with this contract choice.By changing the time at which you use electricity, you may be able toreduce your program bills.

“The price of electricity under the time-of-use/critical peak programcontract will vary between three program rates:

“Off-peak: this program rate will apply mid-day, night, and weekendhours when demand for electricity is typically at its lowest. Theprogram rate during these times will be lower than what you currentlypay your local utility.

“On-peak: this program rate will apply in the weekday early morning andearly evening hours when the demand for electricity is typically at itshighest. The program rate during these times will be higher than whatyou currently pay your local utility.

“Critical peak: this program rate will apply during times of powershortages or emergencies on the electrical grid (representingdisruptions on the power grid, times of increased congestion on majortransmission lines, etc.), The program rate during these times will bemuch higher than the ‘on-peak’ rate described above. There will be alimit to the number of these critical peak events for the year, eachlasting no more than four hours, and you will be notified at least oneday in advance so you can respond appropriately.

“Your equipment will receive these price signals, and you can set yourequipment to respond automatically to these rates as you desire. You canalso take voluntary actions to reduce your household's energy use duringcritical peak times. For your convenience and comfort, you can overrideyour equipment settings at any time.

“Tips to minimize your program bill under this program contract:

“Choose the maximum economy selection when setting up your thermostatset points using the Invensys GoodWatts™ user interface.

“Avoid overriding your controller.

“Pay attention to notices of upcoming critical peak price events and beprepared to respond.

“Eliminate all unnecessary use of electricity during critical peakperiods.”

Those residential participants assigned to the time-of-use with criticalpeak pricing contracts were invited to automate their homes' responsesfor on-peak, off-peak, and critical peak periods using theenergy-management equipment supplied them by the project. Prices wereassigned by the project for each of these three periods, and thesetime-of-use prices and corresponding periods remained constant at leastthrough a season. Participants were able to select from multiple comfortsettings, much as has been described for those on real-time pricecontracts. During the on-peak periods, including that for a criticalpeak period, the homes' thermostats would revert to a user-selectedtemperature setback, which would permit the homes' temperatures to coastand avoid or defer energy consumption. Water heater operation could becurtailed during on-peak and critical peak scheduled periods. A criticalpeak event was called only once during the project for a 4-hour periodbetween 2:00 AM to 6:00 AM. Invensys Control GoodWatts system had beendesigned for time-of-use interactions, and the equipment was quickly andeasily configured for participants on this contract.

The time-of-use periods and their retail rates are summarized in Table4.

TABLE 4 Time-of-use and Critical Peak Rates Season Period Times (pacificw/DST) Price (¢/kWh) Spring off-peak 9:00 a-5:59 p; 9:00 p-5:59 a 4.119(1 Apr-24 Jul) on-peak 6:00 a-8:59 a; 6:00 p-8:59 p 12.150 critical (notcalled) 35.000 Summer off-peak 9:00 p-2:59 p 5.000 (25 Jul-30 Sep)on-peak 3:00 p-8:59 p 13.500 critical (called 1 Nov 2:00 a- 35.000Fall/Winter off-peak 9:00 a-5:59 p; 9:00 p-5:59 a 4.119 (1 Oct-31 Mar)on-peak 6:00 a-8:59 a; 6:00 p-8:59 p 12.150 critical (not called) 35.000

“Real-Time Pricing Program Contract—

This choice requires the greatest consumer involvement and the greatestchange in your electricity usage patterns. You are most likely toreceive the largest program payment with this contract choice.

“The price of electricity under the real-time pricing program contractwill vary every five minutes and somewhat unpredictably during thecourse of the day, week, and year. Participants in this program contractcan set and adjust an automatic response to the price signals by goingto the Internet and choosing between maximum comfort, maximum economy,or some level of response in between. At any time, you can press abutton on your thermostat to override your pre-set responses. Someequipment also will signal if prices are unusually high so that you canchoose whether to use electricity or not during that program priceperiod. By using less electricity, especially when energy prices arehigh, you may be able to lower your program bills substantially.

“Tips to minimize your program bill under this program contract:

“Program the Invensys GoodWatts™ thermostats for your heating andcooling system and water heater for maximum economy.

“Avoid overriding your system settings.

“Voluntarily reduce your overall electricity usage as much as possiblewhen the warning light is flashing, indicating that prices are unusuallyhigh

“Perform energy-efficiency strategies to save energy, such as thoselisted above.”

Behaviors of automated controls in the real-time price contract homeswere described in Section III.A for thermostatically controlled spaceconditioning and in Section III.B for water heater controls.

“Control Group—

In addition to these three groups, a certain number of participants willbe randomly assigned to a control group for the course of the program.If you are selected to be in the control group, equipment will beinstalled in your home, but you will not have a program account, programcontact, or program bills. Control-group members will receive $150 overthe course of the project in appreciation of their participation,regardless of how they use electricity.”

Graph 3200 of FIG. 32 shows participants' first, second, and assignedcontract types. As shown, participants showed the strongest preferencefor real-time pricing contracts. This preference was somewhatsurprising, but the project had perhaps oversold the contract by statingthat participants could earn the greatest incentives by participating inthis contract type.

The project's contract assignment methodology first chose thecontrol-group members at random, and then maximized the number ofparticipants that got their first or second choices through an iterativerandom reassignment procedure. A control group assignment was made, butsubjects were not offered the opportunity to volunteer for the controlgroup. According to this method, a score is generated based on thesquare difference between numbers of customers receiving desirableassignments in each contract type. One randomly chosen subject is thenchanged from one group to another, and if the score is improved, thechange is adopted. This process was repeated up to 1000 times until thescore could no longer be improved. The final arrangement was adopted asthe final customer contract membership.

At the conclusion of this assignment process, 49 percent of participantswere granted their first choices, 16 percent received their secondchoice, and the remaining 36 percent were granted neither their firstnor second choices. This last group is deceptively high because itincluded control-group assignments that could not have been requested bythe participants. Final group memberships were nearly equal in sizebetween the four contract groups. See Table 5. The final assignment of aspecific program contract was communicated to each participant.

TABLE 5 Residential Contract Choices Preference Fixed Time-of-UseReal-time None Control first 10% 32% 58%  3% — second 14% 48% 14% 27% —assigned 26% 26% 28% — 23%

7. Residential Control Equipment

The project conducted a competitive request for proposals forenergy-management-system equipment that would perform requestedmonitoring and control functions for residential thermostats andelectric water heaters. Invensys Controls won this competition andentered into a contract to provide the project its needed equipment andservices. Diagram 3300 of FIG. 33 shows the components of the InvensysGoodWatts™ system. The system had been designed primarily fortime-of-use contract types.

Each participant was provided by the project with a: (1) a home gateway;(2) a Virtual Private Network (VPN) for those homes possessing digitalsubscriber line (DSL) broadband connections; (3) a water heaterload-control module; (4) a communicating thermostat; and (5) an advancedrevenue meter. A sample of participants was also provided a load-controlmodule for their clothes dryers.

The system's home gateway communicated wirelessly with the other systemhardware and via Internet to the project and Invensys back-end servers.The gateway and VPN resided near each home's personal computer. Thegateway's firmware contained some of the necessary project functionsthat defined its interactions with the thermostat and water heater. Thegateway's firmware was successfully updated in the field several timesduring the project to update or correct system performance. The gatewaymaintained a memory of component actions and duties such that the systemcomponents could function acceptably for a while even if Internetconnectivity had been severed.

The home gateway required that a VPN box be installed in locationshaving DSL Internet connectivity. Additional thermostats were used aswireless repeaters in several locations where distances or materialsprevented successful wireless communication between system components.The gateways, VPNs, and modems were found to need periodic re-booting bythe participants during the project.

a. Water Heater Load-Control Module

The water heater load-control module used in the experiment contained a240-V AC switch and a means for wireless communications that permit itto receive, store, and respond to the curtailment commands and schedulesthat it receives. The load-control module could also tell the projectwhether the water heater was active or idle as a load. A load module wasinstalled between each electric water heater and 240-V AC home servicein each project home by licensed electricians. Based on users' occupancyschedules, time-of-use schedules, critical peak events, or real-timecontract commands, the load-control module switch could break the 240-VAC circuit, causing the water heater, if active, to shed or defer itsload. A water heater load-control module is shown installed at a projecthome in image 3400 of FIG. 34.

b. Communicating Thermostat

Participants' existing thermostats were replaced by GoodWatts wirelesscommunicating thermostats. The thermostat was able to receivecurtailment commands and was able to maintain simple scheduled occupancymodes with or without wireless connectivity to the remainder of thesystem. The liquid crystal display (LCD) panel of the thermostatdisplayed the system's present occupancy setting and status and alertedthe participant to special occurrences, like high energy priceconditions. At the project's conclusion, participants chose to eitherhave their project thermostats left in place or removed and replaced byanother.

c. Advanced Revenue Meter

A GoodWatts advanced revenue meter was also installed at eachparticipating home by the local utility. This meter kept track of notonly electricity consumption, but also the time during which theconsumption occurred. The discrimination of electrical consumption bytime was a useful component of the project. The meter's present readingcould be polled at any time to provide other functions and confirmationsfor the project.

These meters became the property of the local utilities that installedthem. Installation of the meters by the utilities represented the majortechnical interaction between the project and themselves. Allparticipating project utilities elected to keep the revenue metersinstalled in place at the conclusion of the experiments. Image 3500 ofFIG. 35 shows an installed project revenue meter.

d. Clothes Dryer Module

The project was able to display price information and curtailmentrequests on the front panels of approximately 50 HE² Sears Kenmoreclothes dryers manufactured by Whirlpool Corporation. These clothesdryers were designed to display “Pr” concurrent with high priceconditions and “En” during CPP and traditional curtailment requests.Image 3600 of FIG. 36 shows an example of this indicator feature. Thestarts of these displayed conditions were accompanied by an audiblealert from the dryers. During these displayed conditions, clothes dryerusers were required to push the start button a second time toacknowledge and override the condition alert. Otherwise, no changes indryer performance occurred. Dryer operation was never directlyinterrupted by the project.

The purpose of this project price interaction with dryers was to observehow participants might interact and provide fully voluntary priceresponsiveness at appliances that provide alerts, but no direct controlaction, for the appliance user.

e. Participant GoodWatts Web Site Interactions

Participants were able to view detailed 15-minute energy information forthe historic operation of their thermostats, water heaters, and the restof their appliances at a project website (termed the “GoodWatts Website”). There, participants could review details of their applianceenergy consumption for any 15-minute interval of the project. They couldalso review aggregate consumption histories as well. Images 3700 and3800 of FIG. 37 and FIG. 38 show a participant interacting with hisGoodWatts project Web site, and an example web page that participantsmight see when they enter their Web site.

This Web site too was where participants could set their occupancyschedules and establish how each controllable appliance (e.g.,thermostatically-controlled HVAC, water heater) should behave duringeach occupancy period. Time-of-use contract participants, for example,could establish their appliance behaviors for on-peak and off-peakintervals. All participants received the same energy-managementequipment. However, participants were offered the opportunity toreconfigure the performance of their thermostats and water heatersduring the experiment differently, depending on their assigned contracttypes. The list of available appliance controls by contract type issummarized in Table 6.

Residents in every contract type could specify time-of-day occupancyschedules. The start and stop times of these periods, the desired spaceconditioning thermostat set points, and the on/off status of electricwater heaters could be scheduled for one or more occupancy periods. Anyrestrictions imposed by occupancy schedules overrode responses fromtime-of-use or real-time price schedules or commands. For example, awater heater held off by its user's occupancy schedule was not thenavailable to curtail its load as a price response.

Real-time and time-of-use contract residents were further allowed toconfigure their relative desire for comfort versus economy by selectingcomfort settings for their thermostats and water heaters. Time-of-usecontract customers were able to specify absolute thermostat setbacksthat would apply during peaks and critical peaks and the preferredon/off behaviors of their water heaters too during these times. Thereal-time contract customers, however, selected from seven comfortsetting options (more if pre-cooling options are counted too) for theirthermostats and five comfort setting options for their water heaters.From the customers' perspectives, these options were simply a continuumranking, allowing customers to state their preferences between maximumcomfort (price would not affect thermostat set back) and maximum economy(customers recover as much of their project shadow account as ispossible by allowing large setbacks). These settings, for example,affected the likelihood that real-time contract customers' water heaterswould be permitted to run during consecutive 5-minute intervals.

Control and fixed-price group members could only set their occupancymodes and corresponding thermostat set points. The only differencebetween the control and fixed-contract groups was that the fixed-pricecontract members could achieve an energy-efficiency benefit through theshadow market; control group members could not.

TABLE 6 Appliance Control Summary by Residential Contract TypeThermostats Water Heaters Cost vs. Cost vs. Occupancy TOU/CPP EconomyRelative RTP Occupancy TOU/CPP Economy Relative RT Set Point Set PointSetback Bid an d Set on/off on/off Response Likelihood SchedulesSchedules Options Point Response Schedules Schedules Options Responsecontrol X X fixed X X TOU X X X X RTP X X X X X X

Participants on the real-time price contracts selected one of five waterheater comfort settings and one of seven thermostat comfort settings.These options established acceptable temperature limits and responsecurves for thermostats and likelihood functions for water heaters,respectively. These options are summarized in Table 7. Note that thecomfort-settings options were offered to participants in this way as alimited number of descriptive options. The resulting computations wereperformed, and range parameters were set in the background.

TABLE 7 Appliance Comfort Settings and Resulting kw and kr Values WaterHeater Thermostat Cooling Heating Comfort settings k_(W) ComfortSettings k_(T) _(—) _(L)/k_(T) _(—) _(H) Tmin/Tmax k_(T) _(—) _(L)/k_(T)_(—) _(H) Tmin/Tmax maximum economy 2.0 maximum economy 1/1   0/10^((b)) 1/1  −10/0^((b)) balanced economy 1.5 balanced economy 2/2 0/10 2/2 −10/0  balanced 1.0 comfortable economy 3/3  0/10 3/3 −10/0 balanced comfort 0.5 economical comfort 1/1 0/5 1/1 −5/0 maximum comfort0.0 balanced comfort 2/2 0/5 2/2 −5/0 maximum comfort 3/3 0/5 3/3 −5/0maximum economy^((a)) 1/1 −3/10 1/1 −10/3  balanced economy^((a)) 2/2−3/10 2/2 −10/3  comfortable economy^((a)) 3/3 −3/10 3/3 −10/3 economical comfort^((a)) 1/1 −3/5  1/1 −5/3 balanced comfort^((a)) 2/2−3/5  2/2 −5/3 maximum comfort^((a)) 3/3 −3/5  3/3 −5/3 no pricereaction ∞/∞ 0/0 ∞/∞  0/0 ^((a))with pre-heat and pre-cool option.^((b))T_(min) and T_(max) are expressed as ° F. above or below thepresent thermostat

f. Installation of Project Equipment

The next paragraphs present some unanticipated events and conditionsthat were encountered during equipment installation.

Soon after its first equipment installations, the project learned abouta limitation of components' wireless communication distance. Thislimitation presented the greatest challenges for the revenue meter,which was frequently located on a pole far from the residence at projecthomes. After recognizing this limitation, the project began questioningnew applicants about the distances between their home computers andelectric revenue meters. New applicants were disqualified whenever thisdistance exceeded 60 feet. At some locations, additional thermostatswere successfully employed as wireless repeaters for the systems toovercome this limitation and transmit effectively over longer distances.

The project also encountered a number of homes having service other thanthe desired 200-ampere, split-phase meters that were unsuitable for usewith the chosen energy-management system.

Additional routers were needed in conjunction with the home gatewaywherever DSL home Internet service existed. The Internet communicationswere not entirely stable during the project, requiring the periodicre-booting of gateways and routers, especially after stormy weather.Project personnel often had to monitor these communication outages andhad to consequently phone participants to help them conduct manualre-boots and re-establish connectivity on behalf of the project.

E. Commercial Building Load Control

A market-based control technology for a commercial building's HVACsystem was also implemented and studied. Details of this implementationand study are described above in Section III.C.

F. Municipal Water Pump Load Control

A market-based control technology for controlling municipal waterpump-load resources was also implemented and studied. Details of thisimplementation and study are described above in Section III.D.

G. Distributed-Generator Control

A market-based control technology for controlling generators accordingto a distributed-generated scheme was also implemented and studied.Details of this implementation and study are described above in SectionIII.E.

H. Data Analysis Results

This section presents and describes major analysis findings from theOlympic Peninsula Project.

1. Chosen Equipment Settings

Overall, the thermostat cooling and heating temperatures chosen byresidential participants throughout the project were distributed asexpected for the population, with a few outliers for those thermostatsthat were installed in unusual locations because they were being used bythe energy-management-system vendor as wireless relays. No easy way wasfound to differentiate the thermostats used as communication relays fromthose truly used as thermostats. See graph 3900 of FIG. 39. This figurereflects the participants' chosen thermostat set points (desired heatingand desired cooling) and the thermostat temperature limits (e.g., T_(T)_(—) _(L) and T_(T) _(—) _(H)) that the participants selected throughtheir comfort setting choices. In this figure, the set points and limitsare included for every participant's various occupancy modes (e.g.,sleep, away, return) and for participants in every contract type. Thedata query included initial thermostat settings and all changes in thosethermostat settings configured through participants' GoodWatts Web sitesduring the project. The set points are in no way weighted for thefraction of time the thermostats spent in any of their occupancy modes,which might make the shown temperature ranges appear more spread outthan what was in fact tolerated by participants most of the time.

Real-time price participants were further offered the ability to choosecomfort settings for their water heaters and thermostats. As has beendescribed, this setting allowed the thermostat to place more or lessemphasis on either comfort or economy, using the price fluctuations inthe real-time price to avoid high-price consumption and encouragelow-price energy consumption. A snapshot of the distribution of thesecomfort settings is shown in Table 8. The data query was based on k_(T)values (see Table 7) only and was therefore unable to distinguish allcomfort settings. The table also fails to distinguish those who selectedthe pre-heat and pre-cool option. The data query includes initialsettings and changes in those settings that were requested byparticipants for any of their occupancy modes during the project. Thedistribution also does not weigh the comfort-setting distribution forthe amount of time spent in each occupancy mode or its comfort setting.Most real-time contract participants maintained a balanced comfortsetting intermediate between the extremes. Incidentally, this balancedsetting was also the starting point assigned to all such participants'thermostats at the beginning of the experiment.

TABLE 8 Snapshot Summary of Real-time Contract Participants' ThermostatComfort Settings Maximum Comfortable Balanced Economy/ No Price Economy/Economy/ Economical Reaction Maximum Comfort Balanced Comfort Comfort22% <1%^((a)) 67%^((a)) 10%^((a)) ^((a))Table does not distinguishcomfort setting modes with and without the pre-heat and

While not bidding their value into the real-time market, real-timecontract participants could configure their residential water heaters'comfort settings to be more or less sensitive to electricity pricefluctuations. Among the 28 percent of residential participants on thereal-time contract, 61 percent chose to use some water heater priceresponse, as shown in Table 9. The numbers show the original comfortsettings and all changes in comfort settings for any occupancy modeduring the project. The comfort-setting distribution has not beenweighted for the amount of time spent in each occupancy mode havingthese comfort settings.

By comparing Table 8 and Table 9, it can be seen that participants wererelatively more tolerant of price control for their thermostats thanthey were for their water heaters. Perhaps even more real-time contractparticipants would have chosen price responsiveness for their waterheaters had the project not encountered a water-heater control problemearly in the experiment that caused multiple participants to thereafterdisallow such control by the project. The water heater control issueswere rectified, but participants did not thereafter retry the moreaggressive control options.

TABLE 9 Snapshot Summary of Real-time Contract Participants' WaterHeater Comfort Settings Maximum Balanced Balanced Maximum ComfortComfort Balanced Economy Economy 39% 50% 4% 7% 0%

2. Network Performance

The project relied on two types of telemetry: (1) the broadbandcommunication between the gateways and the project; and (2) the wirelesstelemetry of energy-management system data within a residential premise.The project collected data that permits it to address the reliability ofthe broadband “network” communications.

The reliability of telemetry was essential to the proper operation ofthe real-time market because both the total unresponsive residentialload and the individual real-time bids were dependent on the telemetryreports from participants' meters. Any meter not reporting load withinthe 5-minute period before market clearing was excluded from the virtualfeeder load for that market. Graph 4000 of FIG. 40 shows the dailynetwork performance for the duration of the project.

It can be observed that it took nearly two months at the beginning ofthe experiment to improve the communications to a steady level that wasmaintained thereafter throughout the remainder of the experiment. Thereliability of the network communication of consumption, bid, and pricesignals ranged from about 55 to 80 percent, on average, for the fourcontract groups. The reliability of communications for the four contractgroups clearly differs, but the causes for such differences cannot beeasily assigned. The bandwidth communicated by the equipment of eachcontract type was similar.

3. Residential Incentives and Savings

Each residential participant's incentive account was re-filled at thebeginning of each month. The participant's account balance was thendiminished through that month commensurate with his energy consumptionat his contract's energy price. The amount placed in each account eachmonth was based on the participant's historical energy consumption forthe year or two before the project. Because these accounts addressedonly the marginal costs of energy, small changes in participants'behaviors and the weather caused wild fluctuations in accountremainders. The project found it necessary to correct these accountbalances to adhere to the expectations it had communicated to residentsat the beginning of the project and to stay within project incentivebudgets. This problem would not have occurred had the project affectedparticipants' total bills rather than only the marginal portion.

In theory, the incentive payments should have been based strictly on thebalance remaining after charges were deducted from the income given.However, in recruiting participants, the project had guaranteed that theaverage incentive payment for each contract type would be $150.Control-group members would have no opportunity to make more money,fixed-price members would have minimal opportunity based on overallreduction of energy consumption, time-of-use members would have moderateopportunity to make more money based on shifting their energyconsumption to off-peak hours, and real-time members would have the mostopportunity to make money by selecting aggressive economy options ontheir appliances. Graph 4100 in FIG. 41 shows the extent to which thesegoals were accomplished. Project analysis has not yet resolved why thosein the fixed-price contract group appeared to receive less than thetargeted $150. The average target incentive goal was hit by three of thefour contract groups. The spread in participants' incentive paymentdistribution increased from control contract to fixed, time-of-use, andreal-time price contracts.

The expected participant savings were estimated by computing the balanceof the incentive income remaining after the energy charges werededucted. The incentive account starting balances, and consequently thesavings, were computed based on each participant's historical energyconsumption. Energy charges were computed using the contract type andthe corresponding energy price for the energy consumed at that price.Therefore, a participant who used electricity exactly as he had theprevious years and under his previous contract type should have realizedno savings. This comparison from one pricing contract to a second isdescribed in the industry as revenue neutral. See Graph 4200 of FIG. 42.Control-group participants, as the reference for this comparison, couldreceive no savings. Their project payments were not at all influenced bytheir energy consumption. Participants in the fixed-price contractreceived about 2 percent savings compared to the control group, thetime-of-use group saved 30 percent, and the real-time price contractgroup saved 27 percent. It is interesting to note the skew in thedistribution of real-time savings, with the average monthly savingsbeing somewhat less than for time-of-use, but the median savingssignificantly greater than time-of-use participants' savings. This skewis probably caused by the significantly greater savings incurred bythose individuals in the real-time contracts who selected the mosteconomical appliance options compared to those who selected morecomfort.

4. Utility Billing

Although participants received only incentive checks, the projectcollected energy price and usage data necessary to produce a model ofthe revenue stream for a fictitious utility serving the project'svirtual feeder.

Energy consumption peaked during the winter months, and energy use wasroughly equally distributed over the four contract groups, as seen ingraph 4300 in FIG. 43. Note that real-time group members wereeffectively assigned to the control group early in the project duringApril because of initial operational problems with the real-timethermostat controls.

The differences in mean energy consumption between the contract groupswere small but measurable (Table 10). Time-of-use contract membersconsumed less energy, on average. The real-time and fixed price contractgroups used successively more energy. The variances of thesemeasurements were large. A pair-wise signed-rank test conducted on thisdata confirmed all groups' energy consumption were statisticallydifferent at a 5 percent confidence level or higher.

All participants paid more for electricity in winter months (graph 4400in FIG. 44). However, participants on the real-time price contracts paidboth proportionally more and more on the basis of average energy price(shown in graph 4500 in FIG. 45) than did their counterparts havingother contract types. Graph 4500 in FIG. 45 further suggests that atleast one of the initial project contract price estimates had somewhatmissed its mark. The average real-time retail price probably should haveexceeded the fixed price during the winter months. It did not.

TABLE 10 Mean Daily Energy Consumption per Home (April to December) Meandaily energy Standard Deviation Contract Type Consumption (kWh) Control47 24 Fixed 49 22 Time-of-use 39 29 Real-time 47 26

5. Effect of Wholesale Energy Price

The project adopted the MIDC wholesale price as the base of its dynamiclocal marginal price. This price was received by the project bysubscription to a Dow Jones service. Because the price was published oneday later for the hourly closing prices on the previous day, this pricewas necessarily projected forward and used as if it were availablehourly without delay. Recall that the project's local marginal price wasroughly equivalent to this wholesale price most of the time, wheneverthe project virtual feeder operated well below its distributionconstraint capacity.

Graphs 4600, 4602, 4604, and 4606 in FIGS. 46A-D summarize thewholesale-price behaviors during the project. The dynamics and somelonger-term trends in the wholesale price can be observed, but the MIDCwholesale price was most frequently near $50/MWh. The price is seen toshoot above $400/MWh briefly near the 270^(th) day of the experimentalperiod, and, at the other extreme, the price did indeed fall to andremain near zero at times. The price duration curve of graph 4606 inFIG. 46D is very flat just above the price of $50/MWh. The wholesaleprice was significantly elevated for only about 20 hours of the year.While most project market price control was asserted to manage the localfeeder constraint and thereby improve the efficient use of the localinfrastructure, it should be observed that the project's market alsonecessarily responded to these few hours of high wholesale price, which,one would assume, addresses more global grid-wide system efficienciesand constraints. The observed dynamic behavior of the wholesale prices,even without the additional congestion-management values added by theproject's market and local marginal price, suggest that utilities andtheir customers might reap market rewards by tracking even wholesaleprice signals if they can be communicated to utilities and customerspromptly.

6. Residential Load Shapes

Residential participant load behavior was affected by participants'choices of contract. The load shapes for project participants'residences are shown in graphs 4700, 4702, 4704, and 4706 of FIGS.47A-D, where separate figures are presented for the four seasons.“Winter” refers to January through March, “spring” refers to Aprilthrough June, and so on. The figures are also separated out to showweekday (Monday through Friday) behaviors and weekend (Saturday andSunday) behaviors. No special efforts were used to eliminate from thesefigures or report load behaviors for holidays. Additional load shapesare shown in graphs 4708 and 4710 in FIGS. 47E and 47F.

It is no surprise that these Northwest residential loads demonstratewinter peaking with two distinct daily peaks. The largest peak occurs atabout 7:00 AM, and the second, smaller peak, occurs at about 6:00 PM.

Small differences in these load shapes for the entire residential loadcan be seen for the behaviors of the various contract types. Time-of-usewas most effective at reducing peaks for entire residential loads.Indeed, the difference in the time-of-use rate between peak and off-peakrates was a factor greater than 5 and earned a significant response.Time-of-use control, however, resulted in abrupt, not smooth, loadchanges during the start and end of the peak intervals, which wereapplied to the population at the same time, the effect of which could bedetrimental. Furthermore, the fall weekend day graph shows that theimproper assignment of the peak interval (people awaken later on theweekend) can perhaps exacerbate rather than reduce the peak, making itmore pronounced, albeit delayed.

The real-time contract load behavior is perhaps smoothest. The fact thatthe real-time price control is active only when it is most neededimplies that, on average, the real-time price control would not and didnot result in the lowest average peaks. Evidence will be provided laterthat the real-time price control strategy was, nonetheless, effective atreducing congestion peaks when it was important to do so.

The real-time contract group had a shifted load shape for itsthermostatically controlled space conditioning that was directlyresponsive to market price. Graphs 4800 and 4802 of FIGS. 48A-B show theactual and counterfactual thermostat loads for thermostaticallycontrolled space conditioning of real-time contract homes during themost- and the least-constrained periods of feeder control. Because allparticipant bids for real-time price contracts were recorded when themarket cleared, both the actual and counterfactual energy could becomputed for each market period. The actual energy is the power clearedmultiplied by the 5-minute duration of the market. The counterfactualenergy is the energy that would have been used had the market cleared atthe average, not the cleared, price (with a zero price deviation).Therefore, the counterfactual energy is defined as the market intervalmultiplied by the sum of each power that each customer would haveconsumed had the price not deviated (had the market cleared at theaverage price). The counterfactual load curves of FIGS. 48A-B showcredible heating load behavior and also the anticipated behavior ofspace conditioning during the constrained fall period. However, thereal-time market price induced an interesting shift of thethermostatically controlled load whether feeder supply was beingconstrained or not.

When demand was high and the system was constrained, the shift ofreal-time demand to off-peak hours was significantly larger because ofthe large local marginal market price differential between off-peak andpeak hours. Recall that the real-time price contract tracks a dailyaverage local marginal price and therefore allows, or even encourages,energy consumption in the early morning when daily prices are lowest.Some participants selected comfort settings that further exaggeratedthis shift by pre-cooling or pre-heating their homes by up to 3° F. whenmarket prices were much below average. This shift occurred on bothconstrained and unconstrained days. On unconstrained feeder days, pricevolatility moderated, and the thermostats (responding to numbers ofprice standard deviations above or below the average price) becameincreasingly sensitive to smaller diurnal price variations. While thetransactive control design did not explicitly predict future price, thediurnal nature of the price itself effectively induced opportunisticpre-heating or pre-cooling more successfully than the project hadanticipated. Other strategies do not use off-peak energy as effectivelyas real-time price-responsive demand does, and therefore, the real-timecontrollers used more energy during those off-peak: hours whenelectricity happened to be a bargain.

While interesting, the energy consumption shift exhibited by thesethermostats was insufficient to visibly shift the load curves for entireRTP contract homes. The heating energy cleared through the biddingprocess was only a fraction of what might be anticipated for the heatingloads of these homes. Thermostats using “no reaction” occupancy modes,for example, did not bid and would therefore have diluted the averageenergy consumption of the RTP thermostat population.

The only consistently measurable energy-use impact that can be observedis the energy-use reduction of time-of-use participants during peakhours. The real-time price energy reduction only occurs during actualpeak conditions and cannot be easily discerned in the aggregate loadshape, which includes both peak and non-peak load conditions. Incontrast, the peak time-of-use price signal is applied during certainhours of a day, oblivious to whether electricity truly becomesconstrained during that period.

7. Commercial Load Shape

Graph 2400 of FIG. 24 shows the load shapes for the MSL buildings. TheMSL buildings are office and laboratory facilities with a relativelyconstant load through each day. Some variation, caused by facilityoccupancy, was shown between weekdays and weekends. The shown loadshapes were not broken down by season. Some seasonal variation would beexpected, of course.

8. Feeder Capacity

The virtual feeder capacity constraint was varied two times throughoutthe project to explore the responses of residential and commercial,supply and demand, feeder resources operating under different feedersupply constraints. The feeder constraints are summarized in Table 11.One of the most interesting periods of feeder activity was observedduring late October when the feeder capacity was at its lowest valuerelative to total feeder demand. Graph 4900 and 4902 of FIGS. 49A-B showfeeder demand during two such weeks.

The counterfactual demand was deduced by examining the loads' bids.Knowing the bid strategy used to generate bids and knowing the controlstrategy used to manage the device load, one can deduce what theprevailing conditions were at the time the bid was generated. From this,it can be inferred whether the device would have been running were thereal-time price and market feedback unavailable (i.e., if the load wereresponding to an immutable, average price.)

It can be seen in week 30 (FIG. 49A) that whenever the counterfactualdemand exceeded the feeder capacity, the actual demand was held down fora time, either until system demand decreased or until the economicincentive to start the first distributed generating unit overcame thecost of starting it. At this time, the sum of feeder capacity anddistributed generation was temporarily allowed to exceed the feederlimit. It can be seen in week 31 (FIG. 49B) that this process can resultin flat demand for extended periods, with the demand tracking theavailable generation, rather than the other way around.

TABLE 11 Summary Application of Distribution Capacity Dates Capacity(kW) 1 Apr-22 Sep 1500 22 Sep-8 Dec 500 8 Dec-31 Mar 750

This phenomenon can be easily explained by considering how the actualdemand is determined by the real-time market. When the demand is verylow, the feeder itself is the marginal energy supplier, and the localmarginal price is set at the feeder's bid price-very near the wholesaleenergy cost. Under these conditions, the load fluctuates while remainingbelow the feeder capacity, and the price remains constant, as shown ingraph 5000 of FIG. 50A. However, as the demand increases, the consumersbecome the marginal resources, and the feeder is run at capacity, asshown in graph 5002 of FIG. 50B. Under these conditions, the pricefluctuates, but the load remains constant. If the demand continues toincrease, then at some point, the real-time price raises high enough tostart the first distributed generating unit, which then becomes themarginal supplier. This returns us to the previous condition where theprice is constant, but the load fluctuates, as shown in graph 5004 ofFIG. 50C.

It is interesting to note the decreased effectiveness of the real-timeprice control when severe weather conditions made demand less capable ofresponding to price, as was the case during week 36, shown in graph 5100of FIG. 51. In this case, there were comparatively fewer satisfied loadsbidding on the demand side, and this resulted in much closer tracking ofthe actual to counterfactual demands.

This illustrates the need to have a substantial amount and diversity ofloads that can follow real-time prices under extreme weather conditions.There must remain enough satisfied load that can respond to increasinglyhigh prices under constrained supply conditions. Indeed, the severity ofthe demand-response shortage can be seen when the feeder capacity wasincreased on Friday. Immediately after the relaxation of the feederconstraint, the load exceeded the counterfactual demand for nearly halfthe day until normal operating conditions were restored.

9. Project Peak-Load Reduction

One interesting figure of merit for the project is the actual reductionin peak load observed during the experiment. Graphs 5200, 5202, and 5204of FIGS. 52A-C provide an interesting estimate of effective peak-loadreduction during each of the three imposed feeder constraints. Theseparations between the actual and counterfactual curves becomeincreasingly greater near the peak load (toward the left of each graph)and for the progressively more constrained feeder operating conditionsfrom 1500 kW (FIG. 52A) to 500 kW (FIG. 52C). A plateau occurs in theactual load curves at each respective constraint magnitude, where loadis actively being deferred to manage the feeder constraint. The widthsof these plateaus represent the duration for which the loads acted asmarginal market resources reducing capacity and holding the localmarginal price constant near the wholesale price. Operation to the leftof these plateaus eventually required that distributed generators beincluded into the generation mix. Other plateaus appear where the loadsare perhaps managed as marginal resources, again to avoid calling uponsecond or third distributed generators to run. The reduction of peakload appears to have been about 5 percent for the 750-kW constraintperiod and up to 20 percent for the 500-kW constraint period. Accordingto these load-duration graphs, there was about a 5 percent increase in“peak” load for the 1500-kW unconstrained period, a result which theproject cannot yet explain.

The power flowing through the feeder distribution line divided by howmuch power would have flowed through the line had the demand responseand distributed generators not been operating will be called “peakreduction.” The project estimated the peak reduction achieved in theentire feeder by the project's control of a limited number of theresidential, commercial, and municipal resources on the feeder. Thesummary for these peak reduction estimates is found in Table 12. Theproject achieved impressive 19 percent and almost 30 percent averagepeak reductions for the 750- and 500-kW constraints, respectively. Nopeak reduction is estimated for the remaining 1500-kW feeder constraintcondition, which never experienced challenging feeder congestionconditions and needed no peak management.

TABLE 12 Average Peak Reduction during Constrained Project PeriodsPeriod Constraint Mean Reduction Sigma Fall 500 kW 29.7% 18.7% Winter750 kW 19.0% 9.7%

Graph 5300 of FIG. 53 shows the weekly peak reductions when the projectconstrained the feeder. The counterfactual (“would have been”) load wascalculated by using the buy bids to compute what the loads would haveconsumed had the market cleared at the average daily price with novariance. Since no distributed generators would have operated for thecounterfactual, their operation was always excluded from thecounterfactual projection. During the other two more constrainedperiods, peak reductions for many weeks greatly exceeded the reportedaverages.

One important result for the project was its successful management offeeder power constraints under peak load conditions, as is shown ingraphs 5400, 5402, and 5406 of FIGS. 54A-C. Whenever the feeder becameconstrained, additional supply was offered to, and in some casesdelivered to, the load by distributed generators from within the feeder.The capacity of these distributed generators was seamlessly offered andcleared through the project's market. For the 500-kW feeder, the peaktotal demand bid capacity was 1,264 kW, and the peak cleared supply was901 kW. For the 750-kW feeder, the peak total demand bid capacity was1,280 kW, and the peak cleared supply was 1,138 kW. For the 1500-kWfeeder, the peak demand bid and peak cleared supply were both 649 kW.Note that the virtual feeder itself was successfully managed to remainunder its imposed distribution capacity limit (i.e., 500, 750, or 1500kW) for all but one brief interval. In only one instance (under the500-kW feeder) did the market fail to clear because the total supplyoffer was less than the portion of the demand bid from unresponsive,uncontrolled loads. During that single 5-minute period, the feedersupplied 520 kW, which was 20 kW (0.2 percent) over its limit.

10. Consumer Surplus

“Consumer surplus” is that excess portion of satisfied load bids thatexceeds the eventual closing market price in a two-sided market. In thissense, it can be represented by the shaded region in the market closingdiagram 5500 of FIG. 55. It represents the bids from experimentparticipants that were “left on the table” unclaimed by the utility. Theconsumer surplus is the basis of an argument for price differentiation.The utility can capture more revenue if it can differentiate its serviceand price accordingly for supplying the highest bidding customers whoconsistently bid at the top left of the shown load curve.

The project examined the consumer surplus for both residential andcommercial real-time participants. The residential consumer surplus wasvery small compared to the commercial consumer surplus, as is shown ingraph 5600 of FIG. 56. This result was unexpected given that commercialloads are often given a discounted differentiated price, the opposite ofwhat is suggested here.

The discrepancy between consumer surpluses of the commercial andresidential load populations can be explained as follows: in fact,available resources for demand response at the commercial level weresmall compared to total demand of commercial buildings. The controlsystem used the real-time price market signal to control the variableair volume dampers of commercial HVAC. From December through March,price also controlled the commercial electric boiler. However, thegenerating units adjacent to the commercial building could not be runon-grid, forcing those generator units to bid on the demand-side, andonly for the displaceable load value of the served building load. Theprice of those distributed generator load bids was for the generatorstart-up with a minimum runtime 30 minutes, which was typically verymuch greater than the clearing price of the market. This suggests thatthe presence of the non-synchronous generators on the load side of themarket artificially inflated the apparent bids of the commercial loadentities and thus the magnitude of the commercial consumer surplus.

It should also be stated that commercial entities have more market cloutthan do residential customers and might have superior opportunities tochange from one electricity supplier to another. This additional marketforce might entice suppliers to hold electricity prices low, even ifcommercial consumer surplus is shown to be high.

The consumer surplus expressed by hour of day and seasonally, as shownin graph 5700 of FIG. 57, reveals the degree to which consumer surplusvaries during peak demand periods. Seasonally, the load and resourcemarket lines intersect more steeply during fall and winter, thusincreasing the consumer surplus during much of the day. However; duringthe peak heating hours, the consumer surplus diminishes with higherclosing prices. This observation confirms that the real-time pricecontrol does indeed capture the economic value of demand for the utilityduring peak periods.

11. Production Dispatch

Distributed generating units were dispatched based on whether their bidscleared the market. The peak distributed generation dispatch in December(graph 5800 in FIG. 58A) is most likely due to the extreme shortage ofwholesale power imposed on the project's feeder until December 8. Afterthat date, the feeder capacity was increased from 500 to 750 kW, andthus less generation dispatch was required. The peak distributedgeneration dispatch hour was around 8:00 AM, with a smaller peak around6:00 PM (graph 5802 in FIG. 58B). This observation coincides well withthe demand load shapes presented earlier.

12. Contract Type Mixtures for Achieving Desirable Risk/Benefit Ratios

An innovative analysis approach was developed by and applied to theproject market results. In this approach, analysis tools common for theselection of asset portfolios are applied to mixes of price contracttypes.

a. Efficient Frontiers

The concept of efficient frontiers was introduced in 1952 by Nobel Prizewinner Harry Markowitz (1952) as part of the Capital Asset Pricing Model(CAPM) for portfolio theory. The principle is that combining severalstocks into a portfolio can decrease the overall risk below that of anyindividual stock while still attaining a comparable return.

Diagram 5900 in FIG. 59 depicts this idea. The area in green shows allpossible ways (weightings) to combine a group of stocks to make up aportfolio. The top leading edge of this diagram, the efficient frontier,provides the optimal combinations (weightings) of these stocks. This topand left boundary provides the highest return for the lowest risks. Noperson should wish to invest in a portfolio below the efficientfrontier. From below the efficient frontier, the return can always beincreased without increasing the risk, or analogously, decrease therisk, for the same return.

For the stock market, risk is defined as the volatility of a stock. Inits truest form, diagram 5900 shows that any number of normal randomvariable distributions combine to form a unique random variabledistribution. The optimal way to combine any set of normal randomvariables can be determined.

The project poses the question, “Given several types of markets that canbe offered to customers, what is the optimal combination of thesemarkets to offer?” The Olympic Peninsula Project compared threeprinciple market types: a fixed-price contract, a time-of-use contract,and a real-time price contract. Data obtained over a 1-year period makeup the random variables that are needed to perform efficient frontiercalculations.

The efficient frontier diagrams for contract types do not necessarilyhave the same implications as they do in stock analysis. For example, apoint on the efficient frontier in stock analysis is by definitionconsidered “good”; however, the efficient frontier for contract typeanalyses may be good or bad. This analysis does not provide conclusiveanswers, but rather it provides a rich mechanism to evaluate theconsequences of any given contract type mix. Whether a mix is good orbad depends upon the objectives of the utility.

b. Combining Distributions

Consider two normal distribution curves, each defined by its mean andstandard deviation. Diagram 6000 in FIG. 60 shows these two curves (boldblue). Remember, these two curves represent two different sets of data.For example, the first curve might represent income from selling onlywheat, the second, only barley. What income should be expected byselling both wheat and barley? The green normal distribution functionsresult. There are many of these curves, each representing a differentmix of wheat and barley. Together, all these curves represent allpossible income levels obtainable by selling different combinations ofwheat and barley.

It might be assumed that the mean value of each curve would simplyfollow a relatively straight line between the two curves, but as can beseen, that does not happen. More is going on. An efficient frontier hasbeen created. Mathematically, this is simply combining the twoprobability density functions together in different proportions.

Diagram 6100 of FIG. 61 shows another way of viewing this same result.What mixture of wheat and barley should be sold given that the income(mean) and variability in income (standard deviation) of all possibleproportions of wheat and barley are known? If the only goal is toincrease income, then all of the barley should be sold. But what aboutthe variability of the income? This may also be important if regularproceeds from sales are needed to support operations. If, however, thisis not important, then it seems clear that Barley is the way to go.

For argument's sake, let's assume that the income stream is important,so it is desirable to have as consistent an income as possible. A personwould be willing to sacrifice a little profit to make this happen. Inthis case, the optimal mix of wheat and barley occurs at mean 2.6 andstandard deviation near 0.165. Anywhere between this point and point #2(all barley) would be the efficient frontier, which have been denoted assmall circles on this graph. It would be necessary to never drop belowthis optimal point, however, because then a decrease in income wouldaccompany the variability of income.

What if wheat were sold exclusively? Given these observations, byselling a little barley along with the wheat, the income would bothincrease and become more stable.

c. Electric Power Markets

Now we will leave the examples of wheat and barley and consider theelectric power utility industry. In the Olympic Peninsula Project, therewere three types of residential contracts offered to consumers ofelectric power: fixed price, time-of-use, and the real-time price.

Diagram 6200 in FIG. 62 was created from the peak energy data measuredover the duration of the project. Only at the times of the year and theday when energy consumption was high were data used for this analysis.Specifically, the time of year from November 1 to December 8 and thehours of the day from 6 to 9 AM and from 6 to 9 PM were used. These datarepresent the times when the electric power system was at its highestcapacity, and therefore they represent the best time to look at howeffectively the different project contract types influenced the systemcapacity.

An efficient frontier analysis was performed. The shaded surfacerepresents all possible proportions of combining the three contracttypes. The three sharp points at the ends of the shaded regionsrepresent the three pure contract types. For example, the word “fixed”appears near the coordinate (1.04, 1.075). As one moves away from thecorner points in the shaded region, three contact types start mixingtogether. The Olympic Peninsula Project itself had a mixture of roughly⅓ of each contract type, represented on this figure by a red dot.

If a utility wishes to reduce its peak energy use during its times ofhigh capacity, diagram 6200 in FIG. 62 suggests the utility shouldselect a contract mix as low as possible on the peak energy axis. Thispoint happens to correspond to a 100 percent time-of-use contractassignment for this project data. It might be assumed that the utilitywould want the variability also to be low. However, once the peak is lowenough, the utility might want the customers to further be responsive-tochange their energy use as a result of price signals. This implies theutility might actually desire more variability.

By itself, one efficient frontier graph is easily enough interpreted andmight result in a clear suggestion of which mix should be sought, as wasthe case above. However, efficient frontier graphs can be drawn forother parameters, and the optimum mixture of contract types from oneefficient frontier graph and a utility's objectives for that variablemight not at all optimize the utility's objectives for another variable.

Consider another variable and its efficient frontier graph. Gross marginis defined as the revenue generated by the sale of electricity, minusthe cost of that electricity. It does not include costs ofinfrastructure, labor, taxes, overheads, or other fixed costs. It simplygives an early preview of what profits might look like. Omitting theseother fixed charges helps keep this financial metric relevant to a broadrange of companies, all of which can add back in their own unique fixedcharges. Unlike the previous analysis that looked only at peak periodsof electricity use, this gross-margin analysis uses data for theresidential homes for the entire project year, 24 hours per day and 7days per week. Keep in mind that these data are simply differentparameters from this same project.

Diagram 6300 in FIG. 63 is the efficient frontier graph for gross marginfor the duration of the Olympic Peninsula Project. There are still threeextreme locations, but the pure fixed contract point is somewhat hiddenbehind the surface. Both this point and the project's gross margin areemphasized by red dots on the figure. Whereas mixtures heavy intime-of-use contracts minimized energy peaks in the previous analysisand might be preferred, time-of-use contracts also minimize gross marginand would not be preferred in this analysis. This is a clear example ofhow utility objections might create contradictions during efficientfrontier analyses of different parameters.

Regardless, the adoption of this analysis approach shows great promisefor utility selection of contract mixes. This approach clarifies thetradeoffs in satisfying utility objectives and acceptable risk, orvariability, of tradeoffs.

J. Conclusions

The Olympic Peninsula Project investigated “smart grid” technologies forachieving better grid asset utilization and improved systemefficiencies. The project used a futuristic virtual feeder on which itprovided a shadow market. Controllers were provided to the variousmarket participants to automate their preferred responses for theirloads and supplies in response to the market's signals. The shadowmarket induced useful energy price responses from residentialelectricity customers who had been assigned to one of three contracttypes. One contract type was a two-sided, real-time local marginal pricethat cleared every 5 minutes. Commercial buildings and municipal waterpumps also responded. Backup distributed generators provided additionalsupply for the feeder when needed. The project involved a variety oftechnologies from residential, commercial, and municipal customers andfrom both the demand and supply sides. The smart grid technologies wereused in concert, not as isolated technologies.

The project managed a feeder and its imposed feeder constraint usingthese technologies. While they did not truly reside on the same OlympicPeninsula feeder, the project was able to control and monitor arealistic set of supply and demand resources as if they resided on thesame feeder—a virtual feeder. To conserve project expense and time, theproject also defined some virtual generator resources to bolster thesupply available from the feeder's real backup generators. Adistribution constraint was then imposed on the energy that could beimported into the virtual feeder from existing distribution lines-muchlike the real transmission constraint that presently limits transmissiononto the Olympic Peninsula. Three different constraint magnitudes wereimposed from 1500 kW, which never truly constrained the feeder, to 500kW, which severely constrained the feeder. The project marketeffectively deferred loads and invited distributed generation supply torun to successfully hold the distribution below its imposed constraint.For only one 5-minute interval did the project allow the constraint tobe exceeded when total feeder supply was temporarily unable to supplythat part of the feeder load that was uncontrolled by, and thereforeunresponsive to, the market.

Market-based control was investigated as a tool for obtaining usefulprice-based responses from single premises. Zones within PNNL's officeand laboratory facilities in Sequim, Wash., were made to compete for theright to receive conditioned air using a local version of market-basedcontrol. Thermostatically controlled zones permitted their effective setpoints to be adjusted relative to changes in market prices. While ableto bid directly into the project's market, the zones nonethelessresponded to the cleared market price and thereby helped fulfill feederenergy objectives—namely, management of the feeder constraint.Temperatures were automatically set back during constrained feederconditions.

Market-based control was investigated as a tool for obtainingprice-based responses for the entire feeder. Market-based control wasalso implemented on the entire project feeder for control of load andsupply that could respond to the project's two-way, real-time market.Price became the common language by which values of load and supply werebid into the market every 5 minutes. As the loads bid the value of theirpresent need and as supply, including the supply from the distributionfeeder line, offered energy at its costs, the cleared electricity pricequite naturally rose as the constraint feeder capacity was approached.At the higher price, loads deferred their consumption, and somedistributed generators eventually won the right to supply their energyonto the feeder. The market was built upon the region's wholesaleelectricity market (MIDC) and therefore also was affected by andresponded to the more global balance of supply and demand on the largergrid. The deferral of system load at these constraint capacities becameapparent on the project's load duration curves, which exhibited steppedplateaus wherever the system load became deferred.

Peak load reduction was also investigated. A mixture of price signals,including real-time and time-of-use, were provided and affectedelectricity consumption on the project's feeder. A comparison of theresulting average residential load shapes for residential participantsrevealed some interesting characteristic differences. For example,abrupt changes were observed in the time-of-use load shape at the startand stop of peak intervals. The small population size prevented theproject from making more direct comparisons of peaks for the differentresidential contract types. Indeed, the control objectives of thereal-time and time-of-use contract types were noted to be quitedifferent. Because bids were recorded from participating loads andgenerators, a “counterfactual” baseline could be calculated and used forcomparison. The project's load-duration curves for the 750-kW and 500-kWconstraint periods suggest that their worst peaks were diminished byabout 5 and 20 percent, respectively, in comparison with this baseline.Although average energy consumption during the project was similaracross the participants having the various contract types, time-of-usecontract members also reduced their total energy consumption more thandid members of the other contract groups and thereby achievedconservation benefits in addition to their off-peak savings.

Internet-based communications were investigated for use in controllingdistributed resources. Residential participants were required to supplybroadband Internet connections on which the home gateway of theproject's energy-management system could communicate. While the projectexperienced poor average Internet connectivity (55 to 80 percent) andexperienced particularly poor connectivity after regional storms, theInternet control overcame these obstacles. With very few exceptions,upon losing Internet connectivity, the distributed resources performedappropriately in a default mode until the connections could bereestablished. The project found nothing in this respect that shouldprevent scaling up this investigation to full implementation.

Residents eagerly accepted and participated in price-responsive contractoptions. Residential participants were provided educational materialsthat described their project equipment and how the equipment could beconfigured to earn incentives from the project. The participantsappeared to understand and eagerly requested the price-responsivecontract options, including real-time and time-of-use contracts. Afterparticipating in the project, 73 percent of the participants said theywould select a price-responsive contract type if given the futureopportunity. During the closing survey, 95 percent of residentialparticipants said they would be likely or very likely to participate ina similar project in the future. Eighty percent of participants were atleast somewhat satisfied with the residential energy-managementequipment that had been provided to them by the project.

Automation was particularly helpful for obtaining consistent responsesfrom both supply and demand resources. Participants tended to spend verylittle time managing, or even considering, the ways they usedelectricity. Indeed, 55 percent of final survey respondents did notrecall to which project contract group they had been assigned. This is astrong endorsement for automated controllers that can be set once andforgotten. Once configured, automated settings will not likely bechanged by participants unless their appliances cause them to becomeinconvenienced (e.g., cold, delayed, annoyed). The project apparentlyexperienced this response when water heater controllers oncemalfunctioned, and annoyed participants responded by thereafterpreventing any control actions by their water heaters. Project monetaryincentives were insufficient for these participants to later reconsidertheir decisions and re-try the more economical water heater comfortsettings.

The interaction between automation and human volition was alsoinvestigated. Some traditional time-of-use programs have relied only onparticipants' memory to turn off non-critical loads during peak times.The Olympic Peninsula Project was closer to the other extreme, wheremost energy responses were carried out automatically. Between theseextremes, a sample of project clothes dryers warned their users whenprices were high.

Automation was perhaps even more important for the larger commercial andmunicipal loads and sources. Through automation, even critical resources(like the energy in the top few feet of a municipal water reservoir)could be controlled. Such resources could never be controlled usefullywithout automation. Exciting opportunities perhaps lie unused for fastautomation to provide spinning reserve and regulation and perhaps othervaluable ancillary services.

The friendliness with which the project invited and practiced demandresponse may be useful to attaining needed resource magnitudes. Theproject provided all participants and resource operators a means bywhich they could temporarily override the control asserted by theproject. In practice, very few participants appeared to have assertedtheir right to override project control. The project also requesteddecisions from participants in relative terms that they could easilyunderstand and use. While participants might be comfortable stating anexact zone temperature preference, few are sophisticated enough to statea desired tradeoff between electricity price and thermostat setback.Teaching such formulas to all participants would not be productive.These same participants were, however, intuitively capable of selectingfrom among relative comfort settings stated as “maximum comfort” or“balanced economy,” for examples. The energy information available toall project participants on the Web was well received. Electricityconsumers will make better electrical-energy decisions if they are givenuseful feedback. A monthly energy bill is not sufficient feedback.

Real-time price contracts especially shifted thermostatically-controlledloads to take advantage of off-peak opportunities. An interesting shiftin the electricity consumption of real-time price contract thermostatswas observed. Because the thermostats tracked average price and standarddeviations in that price, electricity consumption was advanced to earlymorning hours when electricity is a bargain. During unconstrained days,the thermostat took advantage of the diurnal variations in wholesaleprice. During constrained days, the thermostat “learned” to avoid pricymid-morning local marginal prices. While pre-heating and pre-coolingwere not explicitly designed into the thermostats (they had no explicitpredictive ability) their loads were effectively shifted to emulatepre-heating and pre-cooling. The magnitude and pattern of this loadshift exceeded the project's predictions. These thermostatsovercompensated to correct system peaks. Automatic temperature setbacksover the range prescribed by participants helped flatten system load.These benefits were not easily compared against time-of-use benefits,the response of which is not always so well aligned with true systemconstraints.

Municipal water pumps were also incorporated into the responsive demandmix. The project achieved price-responsive control of five municipalwater pumps. After negotiating with water department representatives whobear the ultimate responsibility to verify that their reservoirs remainfull, the project was allowed to affect operation of the pumps, biddingthe value of and controlling only the top several feet of tworeservoirs' water levels. The water-system operators were provided thenecessary automation and the ability to override project control at anytime. The limited range of operation that was permitted by systemoperators perhaps reduced the effectiveness of this project resource,but many such municipal-load resources exist that might become priceresponsive if the control method can be standardized and eventuallytrusted by municipalities and their system operators.

While understandably constrained by environmental concerns, theproject's real and virtual distributed generators effectively preventedthe overloading of a constrained feeder distribution line during peakperiods. The project controlled two backup diesel generators (175 and600-kW) through their automatic transfer switches and one gasmicroturbine (30-kW) that ran in parallel with the grid. The dieselbackup generators bid the capacities of the office building loads theyprotected; the microturbine bid its nameplate capacity. These generatorsbid a price for their supply capacity based on their actual fixed andvariable expenses. Startup and shutdown expenses were added to the bidsto deter the generators from cycling too rapidly with the fast 5-minutemarket signals. The environmentally licensed runtime hours wereconstrained by a “premium” bid factor that increased bids proportionalto expended licensed hours and remaining license term. To conserveproject expense, several additional distributed generators were emulatedon the project's virtual feeder, operating like the real generators withsimilarly imposed constraints. These generation resources—virtual andreal alike—were called on multiple times during the project to supplyelectricity that could not be supplied by the constrained distributionfeeder. It was shown that these distributed generators—even groups ofemergency backup generators like those found behind many commercialbuildings—could be configured to offer their supply, biddable as areal-time resource into a local marginal price market

Modem portfolio theory was applied to the mix of residential contracttypes and should prove useful for utility analysis. Researchers appliedmodem portfolio theory to the analysis of mixtures of utility contracttypes. As was shown in this report, portfolio theory provides ananalytic structure for better understanding the interplay of utilityobjectives, some of which conflict or compete with one another. Much asone benefits by owning diversified stocks, a utility benefits byoffering a diversified set of energy contract types. Any mixture ofcontracts reduces the overall operational variability that a utilityaccepts below that of anyone single contract type. The practice ofportfolio theory suggests optimal mixtures of these contracts, describedin the theory as efficient frontiers.

Price market participants responded to incentives offered through ashadow market. The project offered real monetary incentives toparticipants for their desirable responses to the project's pricesignals. A shadow electricity market was implemented, in whichparticipants' accounts were filled each month and thereafter depletedcommensurate with the participants' electricity consumption. Those whoresponded most to the price signals received the greatest cashremainders from their accounts. After fully implementing the shadowmarket, the project realized the approach itself might be innovative.This approach permitted the conduct of a field experiment while avoidingdelays from regulatory commissions and their processes. Participantsfully agreed to the terms of participation. The project did not in anyway affect the existing contractual agreements, bills, and paymentsbetween participants and their local utilities. The only downfall of theapproach was that in providing this shadow market, the project was ableto compensate only changes in participants' energy behaviors. Therefore,the effects of weather and other factors that could affect participants'electricity consumption beyond the control of the project were amplifiedand varied wildly. This variability prevented the project from providingparticipants with the real-time feedback concerning the status of theirshadow market accounts that it had hoped to provide. This observedvariability would diminish if the project were to have affectedelectricity customers' entire bills rather than only the marginalchanges in those bills.

In view of the many possible embodiments to which the principles of thedisclosed invention may be applied, it should be recognized that theillustrated embodiments are only preferred examples of the invention andshould not be taken as limiting the scope of the invention. Rather, thescope of the invention is defined by the following claims and theirequivalents. We therefore claim as our invention all that comes withinthe scope and spirit of these claims.

What is claimed is:
 1. A method for generating a bid value forpurchasing electricity in a market-based resource allocation system,comprising: by computing hardware, receiving an indication of a currentstatus of a system controlled by an electrical device; computing anaverage dispatched value using multiple dispatched values from aprevious time period, the multiple dispatched values representing valuesat which electricity was dispatched by the market-based resourceallocation system during the previous time period; computing a bid valuefor purchasing electricity sufficient to operate the electrical device,the computing being performed using at least the current status of thesystem and the average dispatched value; transmitting the bid value to acentral computer in the market-based resource allocation system;receiving from the central computer an indication of a dispatched valuefor a current time frame; comparing the bid value to the dispatchedvalue for the current time frame; and generating a signal to activatethe electrical device if the bid value is equal to or exceeds thedispatched value for the current time frame.
 2. The method of claim 1,wherein the method further comprises computing a standard deviation ofthe multiple dispatched values from the previous time period, andwherein the computing of the bid value is additionally performed usingthe standard deviation.
 3. The method of claim 1, wherein the methodfurther comprises receiving a user comfort setting selected by a user,the user comfort setting being selected from at least a first usercomfort setting and a second user comfort setting, the first usercomfort setting indicating the user's willingness to pay more to achievea desired status of the system controlled by the electrical devicerelative to the second user comfort setting, and wherein the computingof the bid value is additionally performed using the user comfortsetting.
 4. The method of claim 1, wherein the electrical device is apump and the current status is a measurement of a water level affectedby the pump.
 5. The method of claim 1, wherein the electrical device isan electric charger for charging a battery, and wherein the currentstatus of the system is the state of charge of the battery.
 6. Themethod of claim 5, wherein the bid value is computed according to thefollowing equation:P _(bid) =P _(avg) −kP _(std) SOC _(dev) where P_(bid) is the bid value,P_(avg) is an average daily clearing price of energy, P_(std) is a dailystandard deviation of price, and SOC_(dev) is the fractional deviationof the SOC from a desired SOC (SOC_(des)) with respect to minimum andmaximum limits (SOC_(min) and SOC_(max)) set by a user.
 7. The method ofclaim 1, further comprising continuously repeating the acts ofreceiving, computing the average dispatched value, and computing the bidvalue over fixed periods of time.
 8. The method of claim 7, wherein thefixed periods of time are periods of 15 minutes or less.
 9. The methodof claim 1, wherein the electrical device is an air-conditioning unit;heating unit; heating, ventilation, and air conditioning (HVAC) system;hot water heater; refrigerator; dish washer; washing machine; dryer;oven; microwave oven; pump; home lighting system; electrical charger,electric vehicle charger; or home electrical system.
 10. An integratedcircuit, pump, or electrical charger comprising computing hardwareconfigured to perform the method of claim
 1. 11. A computer-readablestorage device storing computer-executable instructions which whenexecuted by a computer cause the computer to perform a method forgenerating a bid value for purchasing electricity in a market-basedresource allocation system, the method comprising: receiving anindication of a current status of a system controlled by an electricaldevice; computing an average dispatched value using multiple dispatchedvalues from a previous time period, the multiple dispatched valuesrepresenting values at which electricity was dispatched by themarket-based resource allocation system during the previous time period;computing a bid value for purchasing electricity sufficient to operatethe electrical device, the computing being performed using at least thecurrent status of the system and the average dispatched value;transmitting the bid value to a central computer in the market-basedresource allocation system; receiving from the central computer anindication of a dispatched value for a current time frame; comparing thebid value to the dispatched value for the current time frame; andgenerating a signal to activate the electrical device if the bid valueis equal to or exceeds the dispatched value for the current time frame.12. The storage device of claim 11, wherein the method further comprisescomputing a standard deviation of the multiple dispatched values fromthe previous time period, and wherein the computing of the bid value isadditionally performed using the standard deviation.
 13. The storagedevice of claim 11, wherein the method further comprises receiving auser comfort setting selected by a user, the user comfort setting beingselected from at least a first user comfort setting and a second usercomfort setting, the first user comfort setting indicating the user'swillingness to pay more to achieve a desired status of the systemcontrolled by the electrical device relative to the second user comfortsetting, and wherein the computing of the bid value is additionallyperformed using the user comfort setting.
 14. The storage device ofclaim 11, wherein the electrical device is a pump and the current statusis a measurement of a water level affected by the pump.
 15. The storagedevice of claim 11, wherein the electrical device is an electric chargerfor charging a battery, and wherein the current status of the system isthe state of charge of the battery.
 16. The storage device of claim 11,wherein the bid value is computed according to the following equation:P _(bid) =P _(avg) −kP _(std) SOC _(dev) where P_(bid) is the bid value,P_(avg) is an average daily clearing price of energy, P_(std) is a dailystandard deviation of price, and SOC_(dev) is the fractional deviationof the SOC from a desired SOC (SOC_(des)) with respect to minimum andmaximum limits (SOC_(min) and SOC_(max)) set by a user.
 17. The storagedevice of claim 11, wherein the method further comprises continuouslyrepeating the acts of receiving, computing the average dispatched value,and computing the bid value over fixed periods of time.
 18. The storagedevice of claim 17, wherein the fixed periods of time are periods of 15minutes or less.
 19. The storage device of claim 11, wherein theelectrical device is an air-conditioning unit; heating unit; heating,ventilation, and air conditioning (HVAC) system; hot water heater;refrigerator; dish washer; washing machine; dryer; oven; microwave oven;pump; home lighting system; electrical charger, electric vehiclecharger; or home electrical system.
 20. A system for generating a bidvalue for purchasing electricity in a market-based resource allocationsystem, the system comprising: computing hardware configured to receivean indication of a current status of a system controlled by anelectrical device; computing hardware configured to compute an averagedispatched value using multiple dispatched values from a previous timeperiod, the multiple dispatched values representing values at whichelectricity was dispatched by the market-based resource allocationsystem during the previous time period; and computing hardwareconfigured to compute a bid value for purchasing electricity sufficientto operate the electrical device, the computing being performed using atleast the current status of the system and the average dispatched value;computing hardware configured to transmit the bid value to a centralcomputer in the market-based resource allocation system; computinghardware configured to receive from the central computer an indicationof a dispatched value for a current time frame; computing hardwareconfigured to compare the bid value to the dispatched value for thecurrent time frame; and computing hardware configured to generate asignal to activate the electrical device if the bid value is equal to orexceeds the dispatched value for the current time frame.